1 New Directions For NOS Research Gürol Irzik1 Robert

1 new directions for nos research gürol irzik1, robert nola2 1sabancı university, istanbul, turkey; corresponding author: irzik@sab

1
New Directions for NOS Research
Gürol Irzik1, Robert Nola2
1Sabancı University, Istanbul, Turkey; corresponding author:
[email protected];
Phone number: (90)-216-4839348; Fax number: (90)-216-4839250
2University of Auckland, Auckland, New Zealand. [email protected]
Abstract. The idea of family resemblance, when applied to science, can
provide a powerful account of the nature of science (NOS). In this
chapter we develop such an account by taking into consideration the
consensus on NOS that emerged in the science education literature in
the last decade or so. According to the family resemblance approach,
the nature of science can be systematically and comprehensively
characterized in terms of a number of science categories which exhibit
strong similarities and overlaps among diverse scientific disciplines.
We then discuss the virtues of this approach and make some suggestions
as to how one can go about teaching it in the classroom.
Key words. Nature of science; family resemblance; the consensus on
nature of science.
Introduction
============
Calls for the inclusion of the nature of science (NOS for short) into
science education have a long history. A number of distinguished
scientists, philosophers and education theorists such as John Dewey,
James Conant, Gerald Holton, Leo Klopfer, Joseph Schwab, James
Robinson, James Rutherford, Michael Martin, Richard Duschl, Derek
Hodson, Norman Lederman, Michael Matthews and Norman McComas
throughout the 20th century emphasized the importance of teaching
science's conceptual structure and its epistemological aspects as part
of science education (Matthews 1998a; McComas, Clough and Almazroa
1998). Today, science education curriculum reform documents in many
parts of the world underline that an important objective of science
education is the learning of not only the content of science but its
nature.1 The rationale is that scientific literacy requires an
understanding of the nature of science, which in turn facilitates
students' learning of the content of science, helps them grasp what
sort of a human enterprise science is, helps them appreciate its value
in today's world and enhances their democratic citizenship, that is,
their ability to make informed decisions, as future citizens, about a
number of controversial issues such as global warming, how to dispose
nuclear waste, genetically modified food, and the teaching of
intelligent design in schools.2 Allchin expressed this idea
succinctly: “Students should develop an understanding of how science
works with the goal of interpreting the reliability of scientific
claims in personal and public decision making” (Allchin 2011, 521;
emphasis original).
There is a voluminous literature on what NOS is, how to teach it, and
what views of NOS students and teachers hold. The aim of this chapter
is not to review this literature. The interested reader can refer to
other chapters of this handbook and earlier useful surveys
(Abd-El-Khalick and Lederman 2000; Deng 2011 and others; Lederman
2007). Teachers' and students' views of NOS are also beyond the scope
of this chapter, in which we focus exclusively on what NOS is. In the
next section we summarize the consensus NOS theorizing in science
education has produced. Making use of the existing consensus, we then
provide, in section 3, a structural description of all the major
aspects of science in terms of eight categories. Applying the idea of
family resemblance to these categories, we obtain what we call “the
family resemblance approach”. We articulate it in some detail in
section 5. We believe that the family resemblance approach provides a
systematic and unifying account of NOS. We discuss this and other
virtues of the family resemblance approach in section 6. We end the
chapter by making some suggestions about how to use this approach in
the classroom.
We would like to emphasize that the present chapter does not deal with
empirical matters such as what teachers and pupils might understand of
NOS. Rather, our task is one within the theory of NOS: it is to
provide a new way of thinking about what is meant by the “nature of
science”. Nevertheless, we do hope that theorists of science education
and science teachers familiar with NOS discussions will find our
approach not only theoretically illuminating, but also pedagogically
useful.
2. Consensus on NOS
NOS research in the last decade or so has revealed a significant
degree of consensus among the members of the science education
community regarding what NOS is and which aspects of it should be
taught in schools at the pre-college level. This consensus can be
highlighted as follows.
Based on considerations of accessibility to students and usefulness
for citizens, Lederman and his collaborators specified the following
characteristics of NOS: Scientific knowledge is empirical (relies on
observations and experiments), reliable but fallible/tentative (i.e.
subject to change and thus never absolute or certain), partly the
product of human imagination and creativity, theory-laden and
subjective (that is, influenced by scientists’ background beliefs,
experiences and biases) and socially and culturally embedded (i.e.
influenced by social and cultural context).3 They also emphasized that
students should be familiar with concepts fundamental to an
understanding of NOS such as observation, inference, experiment, law
and theory and be also aware of the distinctions between observing and
inferring and between laws and theories and of the fact that there is
no single scientific method that invariably produces infallible
knowledge. Others added that science is theoretical and explanatory;
scientific claims are testable and scientific tests are repeatable;
science is self-correcting and aims at achieving values such as high
explanatory and predictive power, fecundity (fruitfulness), parsimony
(simplicity) and logical coherence (consistency) (Cobern and Loving
2001; Smith and Scharmann 1999; Zeidler and others 2002).
A number of researchers propose a similar list of characteristics by
studying the international science education standards documents.
These documents also indicate substantial consensus on two further
matters: the ethical dimension of science (e.g., scientists make
ethical decisions, must be open to new ideas, report their findings
truthfully, clearly and openly); the way in which science and
technology interact with and influence one another (McComas, Clough
and Almazroa 1998; McComas and Olson 1998). Based on a Delphi study of
an expert group consisting of scientists, science educators and
science communicators, philosophers, historians and sociologists of
science, Osborne and others (2003) found broad agreement on the
following eight themes:
*
scientific method (including the idea that continual questioning
and experimental testing of scientific claims is central to
scientific research);
*
analysis and interpretation of data (the idea that data does not
speak by itself, but can be interpreted in various ways);
*
(un)certainty of science (that is, scientific knowledge is
provisional);
*
hypothesis and prediction (the idea that formulating hypotheses
and drawing predictions from them in order to test them is
essential to science);
*
creativity in science (the idea that since scientific research
requires much creativity, students should be encouraged to create
models to explain phenomena);
*
diversity of scientific thinking (the idea that science employs
different methods to solve the same problem);
*
the historical development of scientific knowledge (i.e.,
scientific knowledge develops historically and is affected by
societal demands and expectations);
*
the role of cooperation and collaboration in the production of
scientific knowledge (that is, science is a collaborative and
cooperative activity, as exemplified by team work and the
mechanism of peer review).
Wong and Hodson (2009, 2010) came up with very similar themes (but
with slightly different emphasis) on the basis of in-depth interviews
with well-established scientists from different parts of the world who
worked in different fields:
*
scientific method (different disciplines employ different methods
of investigation);
*
creativity in science (creative imagination plays an important
role in every stage of scientific inquiry from data collection to
theory construction, and absolute objectivity in the sense of
freeing oneself from biases completely is impossible);
*
the importance of theory in scientific inquiry (scientific
activity is highly theoretical);
*
theory dependence of observation (scientific data is theory laden
and can be interpreted in various ways);
*
tentative nature of scientific knowledge (science does not yield
certainty);
*
the impact of cultural, social, political, economic, ethical and
personal factors on science (such factors greatly influence the
direction of scientific research and development and may cause
biased results and misconduct), and the importance of cooperation,
peer review and shared norms (such as intellectual honesty and
open mindedness) in knowledge production.
The overlap between the findings of these studies indicates a
substantial consensus regarding NOS among education theorists.
However, there has been some debate as to whether processes of inquiry
(such as posing questions, collecting data, formulating hypotheses,
designing experiments to test them, and so on) should be included in
NOS. While Lederman (2007) suggested leaving them out, other science
education theorists disagreed arguing that they constitute an
inseparable part of NOS (Duschl and Osborne 2002; Grandy and Duschl
2007). Indeed, research summarized in the above two paragraphs do cite
processes of inquiry as an important component of NOS.
Of course, much depends on how the various aspects and themes of NOS
are spelled out. Osborne and his collaborators warn that various
characteristics of NOS should not be taken as discrete entities, so
they emphasize their interrelatedness (Osborne and others 2003, p.
711; Osborne and others 2001). In a similar vein, others note that
blanket generalizations about NOS introduced out of context do not
provide a sophisticated understanding of NOS (Elby and Hammer 2001;
Matthews 2011); rather the items within NOS ought to be elucidated in
relation to one another in “authentic contexts”. Accordingly, many
science educators have called for “an authentic view” of science,
which aims to contextualize science and focuses on
science-in-the-making by drawing either on science-technology-society
(STS) studies or on the interviews with scientists themselves about
their day-to-day activities; this underlines the heterogeneity of
scientific practices across scientific disciplines through historical
and contemporary case studies.4
A number of science education theorists also urged that issues arising
from science-technology-society interactions, the social norms of
science and funding and fraud within science all be allotted more
space in discussions of NOS; a focus on these is especially pertinent
when educating citizens who will often face making hard decisions
regarding socio-scientific problems in today's democracies. These
topics have been raised earlier in some detail (Aikenhead 1985a,
19885b; Kolsto 2001; Zeidler and others 2002) and are receiving
increasing attention in recent years, in line with calls for an
authentic view of science.5
3.
NOS categories: A structural description
The consensus on NOS highlighted above reveals that science is a
multifaceted enterprise that involves (a) processes of inquiry, (b)
scientific knowledge with special characteristics, (c) methods, aims
and values, and (d) social, historical and ethical aspects. Indeed,
science is many things all at once: it is an investigative activity, a
vocation, a culture, and an enterprise with an economic dimension, and
accordingly has many features: cognitive, social, cultural, political,
ethical and commercial (Weinstein 2008; Matthews 2011). What is needed
then is a systematic and unifying perspective that captures not just
this or that aspect of science, but the “whole science” (Allchin
2011). This is no easy task, and there is certainly more than one way
of carrying it out. Our suggestion is to begin with a broad
distinction between science as a cognitive-epistemic system of thought
and practice on the one hand and science as a social-institutional
system on the other. This distinction is actually implicit in the
aspects of NOS expressed (a) through (d) above: science as a
cognitive-epistemic system incorporates (a), (b) and (c), while
science as a social-institutional system captures (d). We hasten to
add that we intend this as an analytical distinction to achieve
conceptual clarity, not as a categorical separation that divides one
from the other. In practice, the two constantly interact with each
other in myriad ways, as we will see.
1.
Science as a cognitive-epistemic system
We spell out science as a cognitive-epistemic system in terms of four
categories obtained by slightly modifying (a)-(c): processes of
inquiry, aims and values, methods and methodological rules, and
scientific knowledge. We explain these categories briefly below.6
1.
Processes of inquiry.
This include posing questions (problems), making observations,
collecting and classifying data, designing experiments, formulating
hypotheses, constructing theories and models, comparing alternative
theories and models, etc. (Grandy and Duschl 2007).
2.
Aims and values.
This will include items such as prediction, explanation, consistency,
simplicity, and fruitfulness; these are among the well-known aims of
science recognized in the science education literature, as we saw in
the previous section. With regard to prediction and explanation, we
would like to make two points, which the science education literature
tends to neglect. First, scientists value novel predictions more than
other kinds of predictions because novel predictions of a theory give
greater support to it than those that are not (Nola and Irzik 2005,
245-247). (A prediction is novel if it is a prediction of a phenomenon
that was unknown to the scientists at the time of the prediction.)
Second, although there are different kinds of explanations and
therefore different models of explanations, all scientific
explanations are naturalistic in the sense that natural phenomena are
explained in terms of other natural phenomena, without appealing to
any supernatural or occult powers and entities (Lindberg 1992, chapter
1; Pennock 2011).7
Other aims of science include: viability (von Glasersfeld 1989); high
confirmation (Hempel 1965, Part I); testability and truth or at least
closeness to truth (Popper 1963, 1975); empirical adequacy (van
Fraassen 1980). Aims of science are sometimes called
(cognitive-epistemic) values since scientists value them highly in the
sense that they desire their theories and models to realize them (Kuhn
1977). Values in science can also function as shared criteria for
comparing theories and be expressed as methodological rules. For
example, we can say that given two rival theories, other things being
equal, the theory that has more explanatory power is better than the
one that has less explanatory power. Expressed as a methodological
rule, it becomes: given two rival theories, other things being equal,
choose, or prefer, the theory that is more explanatory. Similar rules
can be derived from other values. These enable scientists to compare
rival theories about the same domain of phenomena rationally and
objectively (Kuhn 1977).
3.
Methods and methodological rules.
Science does not achieve its various aims randomly, but employs a
number of methods and methodological rules. This point emerges clearly
in many studies on NOS. Historically, there have been proposals about
scientific method from Aristotle, Bacon, Galileo, Newton to Whewell,
Mill and Pierce, not to mention the many theories of method proposed
in the 20th century by philosophers, scientists and statisticians. For
many of them, deductive, inductive and abductive reasoning form an
important part of any kind of scientific method. Additional methods
for testing hypotheses include a variety of inductive and statistical
methods along with the hypothetico-deductive method (Nola and Sankey
2007; Nola and Irzık 2005, chapters 7-9). The idea of scientific
methodology also includes methodological rules; these have not
received sufficient attention in the science education literature.
Methodological rules are discussed at length by a number of
philosophers of science such as Popper (1959) and Laudan (1996,
chapter. 7). Here are some of them:
*
construct hypotheses/theories/models that are highly testable;
*
avoid making ad-hoc revisions to theories;
*
other things being equal, choose the theory that is more
explanatory;
*
reject inconsistent theories;
*
other things being equal, accept simple theories and reject more
complex ones;
*
accept a theory only if it can explain all the successes of its
predecessors;
*
use controlled experiments in testing casual hypotheses;
*
in conducting experiments on human subjects always use blinded
procedures.
Two general points about scientific methods and methodological rules
are in order. First, although they certainly capture something deep
about the nature of methods employed in science, it should not be
forgotten that they are highly idealized, rational constructions. As
such, they do not faithfully mirror what scientists do in their
day-to-day activities; nor can they always dictate to them what to do
at every step of their inquiry. Nevertheless, they can often tell them
when their moves are, or are not, rational and do explain (at least
partially) the reliability of scientific knowledge. Second, we
presented the above rules of method as if they are categorical
imperatives. This needs to be qualified in two ways. The first is that
some of the rules can, in certain circumstances, be abandoned.
Spelling out the conditions in some antecedent clause in which the
rules can be given up is not an easy matter to do; so such rules are
best understand to be defeasible in unspecified circumstances. The
second is that such categorical rules ought to be expressed as
hypothetical imperatives which say: rule R ought to be followed if
some aim or value V will be (reliably) achieved (see Laudan, 1996,
chapter 7). Often reference to the value is omitted or the rule is
expressed elliptically. For example, the rule about ad-hocness has an
implicit value or aim of high testability. So, more explicitly it
would look like: “If you aim for high testability, avoid making ad-hoc
revisions to theories.” When rules are understood in this way, then
the link between the methodological rules of category 3 and the aims
of category 2 becomes clearly visible.
4.
Scientific knowledge.
When processes of inquiry achieve their aims using the aforementioned
methods and methodological rules, these processes culminate in some
“product”, viz., scientific knowledge. Such knowledge “end-products”
are embodied in laws, theories, and models as well as collections of
observational reports and experimental data. Scientific knowledge is
the most widely discussed category of NOS, as we have seen in the
previous section.
3.2 Science as a social-institutional system
Science as a social-institutional system is investigated less than
science as a cognitive-epistemic system, and for that reason it is
harder to categorize. We propose to study it in terms of the following
categories: professional activities, the system of knowledge
certification and dissemination, scientific ethos, and finally social
values. We discuss them in some detail below, taking into account the
findings of the NOS research on this topic indicated in section 2.
As decades of Science-Technology-Society studies have shown, science
is not only a cognitive system, but is, at the same time, both a
cooperative and a competitive community practice that has its own
ethos (that is, social and ethical norms) and its own system of
knowledge certification and dissemination. It is a constantly evolving
social enterprise with intricate relationships with technology and
with the rest of the society, which both influences and is influenced
by it. Scientists form a tight community and are engaged in a number
of professional activities, interacting both with each other and the
larger public. In short, science is a historical, dynamic, social
institution embedded within the larger society. Categories of science
as social-institutional system can be described as follows.
5.
Professional activities.
Scientists do not just carry out scientific research. Qua being
scientists, they also perform a variety of professional activities
such as attending academic meetings, presenting their findings there,
publishing them, reviewing manuscripts and grant proposals, writing
research projects and seeking funds for them, doing consulting work
for both public and private bodies, informing the public about matters
of general interest, and the like. In this way, they perform various
cognitive-epistemic and social functions such as certifying knowledge
and serving certain social goals. Whether they are engaged in
cognitive-epistemic or professional activities, they are expected to
conform to a number of social and ethical norms. We discuss these
below.
6.
The scientific ethos.
Part of the meaning of the claim that science is a social institution
is that it has its own social (institutional) and ethical norms, which
refer to certain attitudes scientists are expected to adopt and
display in their interactions with their fellow scientists as well as
in carrying out their scientific activities. We call them 'the
scientific ethos' (or, equivalently, 'the ethos of science') for
convenience, a phrase coined by the famous sociologist of science
Robert Merton. However, as we will see below, the scientific ethos as
we understand it is not confined to what is known as the 'Mertonian
norms' in the literature. Merton was one of the first to study the
institutional norms of science in the thirties and formulated some of
them as follows, based on his extensive interviews with scientists
(Merton 1973, chapter 13):
*
Universalism: Science is universal in the sense that scientific
claims are evaluated according to pre-established objective,
rational criteria so that characteristics of scientists such as
ethnic origin, nationality, religion, class, and gender are
irrelevant when it comes to evaluation.
*
Organized skepticism: Scientists subject every claim to logical
and empirical scrutiny on the basis of clearly specified
procedures that involve scientific reasoning, testability and
methodology and suspend judgment until all the relevant facts are
in, and bow to no authority except that of critical argumentation.
*
Disinterestedness: Scientists should evaluate and report their
findings independently of whether they serve their personal
interests, ideologies, and the like. The norm of disinterestedness
has the function of preventing scientists from hiding or fudging
the results of their inquiries even when they go against their
personal biases, interests, and favored ideology.
*
Communalism refers to the common ownership of scientific discovery
or knowledge. The rationale is that science is a cooperative
endeavor: new scientific knowledge always builds upon old
knowledge and that scientific discoveries owe much to open and
free discussion and exchange of ideas, information, techniques and
even material (such as proteins).
Although Merton arrived at these norms through an empirical study, we
should not lose sight of the fact that they can be taken as both
descriptive and prescriptive qua being norms. In other words, they
tell us how scientists ought to behave, not just how they do behave
when they do science. Their normative nature and power is evident from
the fact that scientists often face the sanctions of the scientific
community when they violate them.8
In time, the scientific community has become increasingly
self-conscious of the norms of conduct in science, as a result of
which they have proliferated and been codified under the banner
'ethical codes of conduct'. There is now a whole subfield called the
'ethics of science' devoted to this topic. Among other things, these
norms include the following (Resnik 2007, chapter 2):
*
Intellectual honesty (or integrity): Scientists should not
fabricate, distort or suppress data and should not plagiarize.
They should bow to no authority except that of evidence and
critical argumentation,
*
Respect for research subjects: Scientists should treat human and
animal subjects with respect and dignity. This involves getting
the informed consent of human subjects and not inflicting
unnecessary pain on animal subjects, and the like.
*
Respect the environment: Avoid causing harm to the environment.
*
Freedom: Scientists should be free to pursue any research, subject
to certain constraints (as implied by the previous two ethical
principles, for example).
*
Openness: Scientists should be open to free and critical
discussion and to share ideas, data, techniques and even materials
(such as proteins). They should be willing to change their opinion
when presented with good reasons.
Today many scientific institutions (universities, academies, funding
organizations, etc.) have such ethical codes which they announce on
their websites.
None of this is meant to suggest that there is no misconduct, fraud,
data suppression or misrepresentation, and the like, or fierce
competition, especially for scarce resources such as funding, which
sometimes results in secrecy (the opposite of openness) in science.
Scientists are not saints. Nevertheless, when they violate the norms
of science, they often face sanctions. Science has developed a social
mechanism of certification and dissemination to eliminate or at least
reduce misconduct and promote collaboration among scientists.
7.
The social certification and dissemination of scientific
knowledge.
When a scientist or a team of scientists completes their research,
they are hardly finished with their work. Their findings need to be
published; this requires a process of peer review. When published,
they become public and are now open to the critical scrutiny of the
entire community of relevant experts. Only when they prove their
mettle during this entire ordeal are their findings accepted into the
corpus of scientific knowledge and can, amongst other things, be
taught at schools. This is in a nutshell the social system of
certification and dissemination of scientific knowledge, which
involves the collective and collaborative efforts of the scientific
community (Kitcher 2011, chapter 4). This system functions as an
effective social quality control over and above the epistemic control
mechanisms that include testing, evidential relations, and
methodological considerations, etc. described in section 3.1 They
jointly work to help reduce the possibility of error and misconduct.
8.
Social values of science.
Science embodies not only cognitive-epistemic values, but also social
ones. Some of the most important social values are freedom, respect
for the environment, and social utility broadly understood to refer to
improving people's health and quality of life as well as to
contributing to economic development. Without sufficient freedom of
research, scientific development would be stifled. Respect for the
environment involves both the negative duty of not damaging it and the
positive duty of protecting it by saving biodiversity and reducing
carbon emissions that cause climate change. As a species we are
unlikely to survive if we do not respect the environment. Science that
does not contribute to better lives for people would not enjoy their
support; the social legitimation of science today depends crucially on
its social utility. Social utility then serves as an important social
goal of science.
This completes our description of the eight categories of science
which can be tabulated as below.
Science
Science as a Cognitive-Epistemic System
Science as a Social System
1
Processes of inquiry
2
Aims & Values
3
Methods & Methodological Rules
4
Scientific knowledge
5
Professional Activities
6
Scientific Ethos
7
Social Certification & Dissemination of Scientific Knowledge
8
Social Values
Although we believe that the categories that make up science as a
cognitive-epistemic system are pretty exhaustive, we admit the
possibility that other categories might perhaps be added or new
categories might emerge as science develops. We do not think, however,
that categories of science as a social system is exhaustive in any
way. Nor do we claim that this is the only or the best way of
describing science as a social system. Others may carve it out
differently. Nevertheless, we do believe that it captures an important
part of science as social practice. Similarly, we do not pretend to
have listed all the items that fall under each of the eight categories
above. In fact, we consider them open-ended; that is, the
characteristics of science that fall under each category are not fixed
and develop historically. Overall, we believe that the eight
categories capture the structural features of NOS in a systematic and
comprehensive way.
4. Clarifying the meaning of “nature of science” and the idea of
family resemblance
Although we suggested that the above eight categories characterize
nature of science, we have not explored the meaning of term “nature”
that occurs in that phrase. What do we mean “nature of science”? To
our knowledge, this is a question that is hardly raised in the science
education literature. Here we briefly mention three conceptions of
what such a nature might be.
First, the nature of science could be taken to be the specification of
a natural kind of thing which has an essence, where an essence is a
set of properties which a thing must have and without which it is not
possible for that thing exist and to be that kind of thing. Triangles
have an essence in this sense, but it is very doubtful that science
has an essence of this sort. We can agree with Rorty’s negative answer
to the title of his paper ‘Is natural science a natural kind?” (Rorty
1991, 46-62).
A second suggestion about ‘nature’ is to claim that it is a (small)
set of necessary and sufficient properties that something should
possess if it is to be deemed science. Here strong modal claims found
in the essentialist approach mentioned above are downplayed or
eschewed in favour of the mere possession of the set of features
shared by all sciences and only by them. However, so far all attempts
to define science in terms of necessary and sufficient conditions have
failed. Some have restricted their approach to the nature of science
by focusing narrowly on just the fourth category of science, viz.,
scientific knowledge, and then have attempted to define what is to
count as science as what is verifiable (some positivists) or what is
falsifiable (Popper), and so on.9 This is not the approach we advocate
here in characterising science.
A third approach might be simply to list a number of items falling
under the concept of science without pretending to give a set of
necessary and sufficient properties or to specify essence for science.
Thus one common approach to the nature of science in science education
lists some salient features of science as in section 2. This is also
the approach we have adopted by setting out the eight categories of
science and listing the items that fall under each. However, there is
a problem to be tackled: not all sciences share these features or
items all at once. Indeed, a number of science education theorists
have drawn attention to important differences among scientific
disciplines (Samarapungavan, Westby and Bodner 2006; Wong and Hodson
2009). If some sciences lack some of the features others share, what
justifies the label “science” for them? Merely providing a list of
preferred items is powerless to answer this question.
Luckily, there is a satisfactory answer within philosophy that invites
one to have a quite different approach to what counts as a “nature” in
talk of ‘NOS’. In fact it takes us well away from the three ways of
understanding ‘nature’ listed above in using the important idea of
family resemblance (Eflin and others 1999; Hacking 1996; Dupre 1993).
In a nutshell, the nature of science consists of a set of family
resemblances among the items that fall under the eight categories of
science. In an earlier article, we articulated this approach in some
detail for the purposes of science education (Irzik and Nola 2011). In
this chapter, we develop it further.
The idea of family resemblance was developed by the philosopher Ludwig
Wittgenstein in recognition of the fact that not all terms can be
defined in terms of necessary and sufficient conditions or by
specifying essences or natures (Wittgenstein 1958, sections 66-71). To
see this, compare ‘triangle’ with 'game'. The former can be defined
explicitly as: a plane closed figure with three straight sides. This
definition not only gives six characteristics that specify the
necessary and sufficient conditions for being a triangle, but also
determines the “essence” of being a triangle or the analytic meaning
of the term ‘triangle’. In this definition, those properties that are
shared by all triangles and only by triangles are specified
explicitly. By contrast, Wittgenstein argued, the term 'game' cannot
be defined in this way. Any attempt to define the term ‘game’ must
include games as different as ball games, stick games, card games,
children’s games that do not involve balls, sticks or cards (such as
tag or hide-and-seek), solo games (hop-scotch), mind games, and the
like. Unlike the term ‘triangle’, there is no fixed set of necessary
and sufficient conditions which determine the meaning of ‘game’ and
thus no set of properties that cover all games and at the same time
admit nothing which is not a game.10 Nevertheless, Wittgenstein
argued, all games form “a family resemblance”, forming a complicated
network of similarities, overlapping and criss-crossing. It is these
similarities that justify the use of the term 'game' to all those
diverse activities from baseball to hopscotch.
Consider a set of four characteristics {A, B, C, D}. Then one could
imagine four individual items which share any three of these
characteristics taken together such as (A&B&C) or (B&C&D) or (A&B&D)
or (A&C&D); that is, the various family resemblances are represented
as four disjuncts of conjunctions of any three properties chosen from
the original set of characteristics. This example of a polythetic
model of family resemblances can be generalised as follows. Take any
set S of n characteristics; then any individual is a member of the
family if and only if it has all of the n characteristics of S, or any
(n-1) conjunction of characteristics of S, or any (n-2) conjunction of
characteristics of S, or any (n-3) conjunction of characteristics of
S, and so on. How large n may be and how small (n-x) may be is
something that can be left open as befits the idea of a family
resemblance which does not wish to impose arbitrary limits and leaves
this to a “case by case” investigation. In what follows we will employ
this polythetic version of family resemblance (in a slightly modified
form) in developing our conception of science.
Consider the following limiting case. Suppose an example like that
above but in which there is a fifth characteristic E which is common
to all the disjunctions of conjunctions as in the following: (A&B&C&E)
or (B&C&D&E) or (A&B&D&E) or (A&C&D&E). Would this be a violation of
the kind of family resemblance definition that Wittgenstein intended?
Not necessarily. We might say as an example of characteristic E in the
case of games that games are at least activities (mental or physical).
Nevertheless, being an activity is hardly definitional of games, nor
does it specify a criterion of demarcation; there are many activities
that are not games, such as working or catching a bus.
We will see in the case of science that there are characteristics
common to all sciences, but are such that they cannot be definitional
of it. They cannot be used for demarcating science from other human
endeavours either. An example would be observing. We cannot think of a
scientific discipline which does not involve making or relying on
observations at some point. But then not everything that involves
observing is a science (such as being observant when crossing a road
in heavy traffic). Similarly, we cannot think of a science that does
not involve making some kinds of inference at some point; if it did
not, it would not get beyond naive data collecting. Nevertheless, as
before, inferring, though common to the sciences, is not exclusive to
them. Judges in a court or speculators on the stock market make
inferences as well, but they are not doing science.
In the light of these points we can say that there are a few core
characteristics that all sciences share (collecting data and making
inferences, for instance). Nevertheless, even though they are generic,
they are not sufficient either to define science or to demarcate it
from other human endeavours. It is the other characteristics that
accompany observing and inferring that make an important contribution
to the family-forming characteristics that characterize scientific
disciplines. It is this modified version of polythetic family
resemblance that we will employ in what follows.
5. The family Resemblance Approach to Science
There are many items called ‘science’, ranging from archeology to
zoology. (Here we will exclude the special case of mathematics from
our discussion because of its non-empirical character.) So what do
these many things called 'science' have in common? The idea of family
resemblance will tell us that this is a wrong question to ask. What we
need to do is to investigate the ways in which each of the sciences
are similar or dissimilar, thereby building up from scratch polythetic
sets of characteristics for each scientific discipline. The science
categories we have introduced in section 3 will come in handy for this
task.
Begin with the items data collecting, making inferences and
experimenting that fall under the category “processes of inquiry”.
Although all disciplines employ the first two and most (such as
particle physics and chemistry) are experimental, there are a few
disciplines that are not. Astronomy and earthquake science are cases
in point since experiments are simply impossible in these fields. We
cannot manipulate celestial objects; nor can we carry out experiments
in earthquake science by manipulating earthquakes (though there are
elaborate techniques for seismic detection which are not strictly
experimental in the sense of experimentation as manipulation that we
intend). Consider next the category “aims and values” and the item
prediction falling under it. Again, most sciences aim to make
predictions, especially novel ones, but not all of them succeed. For
example, astronomy is very good indeed in predicting planetary
positions. In contrast, even though earthquake science does a good job
of predicting the approximate locations of earthquakes, it fails badly
with respect to predicting the time of their occurrence. Medicine can
statistically predict the occurrence of many diseases under certain
conditions without being able to tell who will develop them and when.
Let us now explore the similarities and differences among various
scientific disciplines in terms of the items under the category
“methods and methodological rules”. Many sciences employ the
hypothetico-deductive method, which can be roughly described as
drawing out observable consequences of theories and then checking them
against observational or experimental data. For example, particle
physics and earthquake science use this method, but there does not
appear to be any place for randomized double-blind experiments in
these disciplines. In contrast, in evidence-based clinical medical
science, the hypothetico-deductive method appears not to be of common
use, while the methods of randomized double-blind experiments are the
ubiquitous gold standard for testing. Similarly, some very important
scientific research projects like sequencing the human genome do not
involve much hypothesis testing, but rather are data-driven, inductive
inquiries where most of the work is done by computer technologies.
Finally, consider the category of scientific knowledge and the items
like laws, theories and models that fall under them. The idea of
family resemblance applies here as well since not all sciences may
have laws. For example, while there are clearly laws in physics, it is
a contested issue as to whether there are laws in biology (Rosenberg
2008).
In the above we have mentioned a number of individual sciences and a
number of characteristics. As can be seen for any chosen pair of these
sciences, one will be similar to the other with respect to some of
these characteristics and dissimilar to one another with respect to
other characteristics. If we think of these characteristics as
candidates for defining science, then no definition in terms of
necessary and sufficient conditions would be forthcoming. If we take a
family resemblance approach, however, things look very different and
promising. Too see this more concretely, let us represent data
collection, inference making, experimentation, prediction,
hypothetico-deductive testing, blinded randomized trials as D, I, E,
P, H and T respectively. Then we can summarize the situation for the
disciplines we have considered as follows:
Astronomy = {D, I, P, H}; Particle physics = {D, I, E, P, H};
Earthquake science = {D, I, P', H}; Medicine = {D, I, P'', E, T},
where P' and P'' indicate differences in predictive power as
indicated.
Thus, none of the four disciplines has all the six characteristics,
though they share a number of them in common. With respect to other
characteristics, they partially overlap, like the members of closely
related extended family. In short, taken altogether, they form a
family resemblance.
Note that in order to convey the core idea that 'science' is a family
resemblance concept, we have so far considered characteristics of
science understood only as a cognitive-epistemic system. Does the idea
of family resemblance apply to science as a social-institutional
system as well? We believe that it does, at least to some degree. All
scientific disciplines have a peer review system and a system of
knowledge certification and dissemination. However, not all of them
share exactly the same social values or the same elements of the
scientific ethos For example, the norm “respect human and animal
subjects” would not apply to disciplines such as physics and chemistry
that do not deal with human and animal subjects, but “avoid damaging
the environment” certainly would. Similarly, although many sciences
serve social utility, there are some fields (such as cosmology and
parts of particle physics such as unified field theory) that are not
obviously socially useful in any way; they are practiced merely to
satisfy our curiosity about the workings of nature. In short, the
sciences form a polythetic family-resemblance set with respect to
their social and ethical dimensions as well.
6. Virtues of the family resemblance approach
We believe that the family resemblance approach to science has several
virtues, both theoretical and pedagogical. Perhaps the most important
theoretical virtue of this approach is the systematic and
comprehensive way it captures the major structural features of science
and thereby accommodates, in a pedagogically useful way, almost all of
the findings of NOS research in science education summarized in
section 2. As we shall illustrate in the next section, both the
categories themselves and the items that fall under them do not dangle
in the air as discrete entities; rather, they are tightly related to
each other in a number of ways, forming an integrated whole. Thus, we
can say that science is a cognitive and social system whose
investigative activities have a number of aims that it tries to
achieve with the help of its methodologies, methodological rules,
system of knowledge certification and dissemination in line with its
institutional social-ethical norms, and when successful, ultimately
produces knowledge and serves society. This generic description is not
meant as a definition of science, but rather as indicating how various
aspects of science can be weaved together systematically as a unified
enterprise.
By including science as a social institution as part of the
family-resemblance approach, the social embeddedness of science
emphasized in the NOS literature in science education is captured in a
novel way. A significant part of what it means to say that science is
socially embedded is to say that non-cognitive values are operative in
science and influence science. No social institution, not even
science, exists in a vacuum, so all kinds of social, cultural,
historical, political and economic factors may influence it. Just to
give an obvious example, funding strongly affects the choice of
scientific problems and research agendas. Non-cognitive factors of all
sorts (gender biases, ideologies, economic considerations, etc.) may
influence data description, hypotheses and even evidential relations
in certain areas such as primatology and research on sex differences,
as noted by feminist scientists and philosophers (Longino 1990).
Sometimes these factors may cause scientists to deviate from the
ethical norms of science (they may, for example, fabricate or suppress
data) and thus have a distorting effect on scientific conduct.
However, not all social factors have a negative impact on science.
Indeed, one of the most important functions of the ethos of science
and mechanisms like peer review along with open and free critical
discussion is precisely to minimize the negative effects on science.
The ethos of science and the social system of scientific knowledge
production contribute to the reliability of scientific knowledge as
much as scientific methods and methodological rules do. In practice,
scientific inquiry is always guided by both cognitive-epistemic and
social-institutional “rules of the game”, so to speak. This gives
substance to our earlier claim that the distinction between science as
a cognitive-epistemic system and science as a social institution is a
conceptual one introduced for analytical purposes; but in practice the
two are inseparable.
The historical, dynamic and changing nature of science can be
accommodated naturally by the family resemblance approach through its
open-ended categories that allow for the emergence of new
characteristics of science within each category. For example, from a
historical perspective we see that many scientific disciplines such as
physics, chemistry, electricity and magnetism became mathematical only
after the Scientific Revolution that occurred in the sixteenth and
seventeenth centuries. Similarly, the hypothetico-deductive method was
first clearly formulated and became established during the same
period. New methodological rules like the one that tells the scientist
to use blind procedures in conducting experiments on human subjects in
life sciences came about only in the 20th century. So did many ethical
norms of science. The family resemblance approach therefore
incorporates the dynamic, open-ended nature of science.
A unique virtue of the family resemblance approach is that it does
justice to the differences among scientific disciplines and yet at the
same time explains their unity by emphasizing the similarities and
partial overlaps among them. It is the existence of these “family
ties” that justify the label 'science' that we apply to various
disciplines from archeology to zoology. The unity of science is a
unity-within-diversity. Earlier we pointed out that observing and
inferring are common to all scientific disciplines even though they
are not unique to the sciences. Another particularly important common
feature of all scientific disciplines is the naturalism inherent in
them—a feature that has not received sufficient attention in the NOS
literature. We have touched upon this in discussing the notion of
scientific explanation in section 3.1 and are now in a position to
articulate it more fully. Science appeals to only natural entities,
processes and events; its mode of explanation, aims and values, ethos,
methods and methodological rules, and the system of knowledge
certification contain nothing that is supernatural or occult.
Scientific naturalism is not an addendum to science invented by
philosophers; rather, it is inherent to science. As Robert Pennock
aptly puts it, it is a “ground rule” of science so basic that it
seldom gets mentioned explicitly (Pennock 2011, p. 184). One of the
important science reform documents that does draw attention to this
aspect of science is The National Science Teachers Association's
statement on NOS: “Science, by definition, is limited to naturalistic
methods and explanations and, as such, is precluded from using
supernatural elements in the production of scientific knowledge”
(quoted from Pennock 2011, p. 197). Scientific naturalism pervades the
whole of science from A to Z. As such, it describes a core aspect of
science that contributes to its unity.
A final virtue of the family resemblance approach is that it is free
of philosophical commitments such as realism, positivism, empiricism,
constructivism, and the like. One can adopt any one of these,
depending on how one wants to spell out each item that falls under
each category of the family resemblance approach. For example, while
realist educators may wish to emphasize truth as an aim of science
both with respect to observable and unobservable entities, those who
are sympathetic to constructivism may settle for viability, provided
that they inform students of the existence of alternative views on
this issue. Thus, they can add content to the family resemblance
approach according to their philosophical orientation or else
completely avoid discussing these philosophical issues due to the
pressure of limited time, the level of the class and so on.
7. Teaching the family resemblance approach: some suggestions
Teaching NOS from the perspective of family resemblance can begin by
introducing the categories of science and then showing how they are
related to one another. A natural place to start is processes of
inquiry since all students are engaged in them to varying degrees. A
host of interesting questions can be pursued in this context. Is
observing a passive activity (raised to illustrate the point that data
collection is often driven by scientific problems and theories)? How
does observation differ from experimentation? What are the different
ways in which a given set of data be interpreted? And so on. Next, the
teacher can explore the connection between processes of inquiry, aims
and hypotheses (or models and theories). This could be motivated very
naturally since processes of inquiry are activities and virtually all
activities have some aim or other. Some of the questions that can be
asked are as follows. What is the point of doing an experiment? How
are observational and experimental data related to hypotheses,
theories and models? Does this theory explain that set of data? How
would an experiment be set up to test some claim? These and similar
questions enable the teacher to make several points: data provide
evidence for or against hypotheses, theories and models; experiments
are conducted to test them; testing can be done (as in the
hypothetico-deductive method) by deducing test predictions from them.
The aforementioned questions also provide excellent opportunities for
the teacher to discuss key scientific notions like “testing”,
“experiment”, “theory”, “law”, and “model”.
Another fruitful question that prompts the exploration of the
relationships among various science categories is to ask how science
achieves its aims. This may lead to the idea of scientific method and
methodological rule. In this context, at least three points can be
made. First, science does not achieve its various aims haphazardly,
but by employing a number of methods and methodological rules. With
their help, science produces reliable (though fallible) knowledge. The
hypothetico-deductive method, in particular, enables students to see
this clearly. Scientific predictions do not always come out right, and
when that is the case, it means that scientists have made a mistake
somewhere and they must revise some of their claims. In this way,
science can eliminate its errors and produce more reliable results.
Second, methods and methodological rules do not dictate to scientists
what to do at every step of their inquiry. A discussion of this point
may help students appreciate the fact that scientific methods and
rules are not mechanical procedures that generate theories (or models)
from data. Hence, theory construction always requires much imagination
and creativity. To stimulate creativity, students may be invited to
come up with different hypotheses that fit or explain the same data.
Third, despite the existence of methods, methodological rules, and
values functioning as criteria for evaluating rival theories,
scientists may sometimes come to reach different conclusions on the
basis of the same body of evidence. This may happen when no single
theory embodies all the cognitive-epistemic values equally well and
when different scientists place different emphasis on them when faced
with a choice among rival theories. One scientist may give more weight
to fruitfulness, say, and another may value simplicity more due to the
priority given to aesthetic considerations (in which case there will
be disagreement about which theory is the better one). A historical
example that comes close to this scenario is the debate scientists had
between Aristotelian-Ptolemaic geocentric system and the Copernican
heliocentric system during the early stages of the Scientific
Revolution. The teacher may discuss this case as example of rational
disagreement among scientists, a disagreement which in no way implies
that they are acting arbitrarily, though they might have subjective
(personal) preferences in weighing values. Properly understood, then,
being subjective does not mean acting arbitrarily, which is the whole
point of Kuhn (1977). In this way, students can see how both personal
(subjective) and intersubjective (objective) factors play a role in
scientific theory choice.
Once the students grasp the categories “processes of inquiry”, “aims
and values” and “methods and methodological rules”, then the fourth
category can be introduced in a straightforward way: scientific
knowledge, especially in the form of theories and models, is the end
product of successful scientific inquiry pursuing the aims of truth,
testability, prediction and the like under the guidance of scientific
methods and methodological rules. The teacher can then draw attention
to and explain the characteristics of scientific knowledge which have
emerged (such as its empirical, objective and subjective nature, its
reliability or tentativeness, its dependence on creativity, and so
on).
As for the teaching of science as a social-institutional system, we
foreground two categories: the scientific ethos and the social
certification of scientific knowledge. What must be especially
emphasized with respect to these categories is their function in
scientific knowledge production. Students must understand that ethical
norms like intellectual honesty and openness and social mechanisms of
peer review, free and critical discussion are as important as
processes of inquiry such as experimenting or in using methods, like
the hypothetico-deductive method of testing, in producing reliable
knowledge. This point can be made forcefully by inviting students to
think about what happens if scientists were to fabricate data or to
accept an idea or a theory without sufficient critical discussion.
8. Conclusion
The main point of this chapter is to suggest a new way of
understanding the term ‘nature’ as it gets employed in the phrase
‘Nature of Science’ (NOS). The word ‘science’ is a broad umbrella term
which, in the context of science education, cannot be
unproblematically captured by proposing accounts of ‘nature’ which are
essentialist, or by specifying a set of necessary and sufficient
conditions for science, Nor can it be captured by drawing up some
small list of features. The problem with a list is that it remains
arbitrary as to why some features are included on the list and not
others; and it remains unclear how, when given such a list, one is to
go on to features not mentioned on the list. Our answer is to suggest
the family resemblance or cluster account of a definition – an account
developed within philosophy to overcome problems with essentialism,
necessary and sufficient conditions and lists already mentioned. As
such our enterprise is more philosophical and is not directed upon
empirical matters such as the kinds of understanding teachers and
pupils might have of NOS, or what level matters pertaining to NOS
might be discussed in classrooms. Nevertheless, the family resemblance
conception of “nature” that we have proposed is not irrelevant to
these empirical matters. What it does is “free up” one’s approach to
them in what we hope is an illuminating way which a too rigid
conception of “nature” might obscure.
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Robert Nola is a professor of philosophy at The University of
Auckland, New Zealand. He has published papers in philosophy of
science, metaphysics, the sociology of science and science education.
His recent books include: with co-author Gurol Irzik Philosophy,
Science, Education and Culture (Dordrecht, Springer, 2005); with
co-author Howard Sankey Theories of Scientific Method (Chesham, Acumen
Press 2007); and with co-editor David Braddon-Mitchell, Conceptual
Analysis and Philosophical Naturalism (Cambridge MA, MIT Press, 2009).
His current work continues in philosophy of science with emphasis on
scientific naturalism and the religion/science conflict.
Gürol Irzik is a professor of philosophy at Sabanci University,
Turkey. He has published papers in philosophy of science, social
aspects of science, and science education. His books include: with
co-author Robert Nola Philosophy, Science, Education and Culture
(Dordrecht, Springer, 2005) and with co-editor Güven Güzeldere Turkish
Studies in the History and Philosophy of Science (Dordrecht, Springer,
2005). He is currently editing a special issue of the journal Science
& Education on the topic of commercialization of academic science.
1 See for example: American Association for the Advancement of Science
1990, 1993; Council of Ministers of Education 1997; National
Curriculum Council 1988; National Research Council 1996; Rocard et al.
2007; McComas and Olson 1998.
2 This point is commonly made, for example, in Driver et al. 1996,
McComas, Clough and Almazroa 1998, Osborne 2007 and Rutherford and
Ahlgren 1990.
3See Abd-El-Khalick 2004; Abd-El-Khalick and Lederman 2000; Bell 2004;
Khishfe and Lederman 2006; Lederman 2004, 2007. Note that all of these
characteristics pertain to scientific knowledge. For that reason,
Lederman suggested replacing the phrase “nature of science” with
“nature of scientific knowledge” in his recent writings (Lederman
2007).
4 See Ford and Wargo 2007; McGinn and Roth 1999; Rudolph 2000;
Samarapungavan, Westby and Bodner 2006; Wong and Hodson 2009, 2010;
Wong et al. 2009.
5 See Sadler 2011; Weinstein 2008; Wong and Hodson 2010; Zemplen 2009;
see also the special issue of the journal Science & Education vol. 17,
nos. 8-9, 2000.
6For a more detailed discussion of these, see Nola and Irzik (2005,
chapters 2, 4, 6-10).
7See Godfrey-Smith (2003) for a succinct summary of different models
of explanations in science.
8STS scholars are generally critical of Mertonian norms and claim that
there is a counter-norm for every Mertonian norm, with the implication
that Mertonian norms do not guide scientific practice and therefore
are simply functionless. See, for example, Sismondo (2004, ch. 3) and
the literature cited therein. However, there are also excellent
critiques of these critiques such as Radder (2010).
9 See some of the following who may be, in addition, critical of the
idea of the demarcation of science from non-science but whose focus in
so doing is just upon the fourth category, viz., what is to count as a
scientific statement: Alters 1997; Hacking 1996; Laudan et al. 1986;
Stanley and Brickhouse 2001; Ziman 2000.
10John Searle has disputed this example, arguing that 'game' can be
defined as follows: a series of attempts to overcome certain obstacles
that have been created for the purpose of overcoming them (Searle
1995, p. 103). However this dispute is resolved, there might still be
other cases where the family resemblance idea gets some traction, as
we think it does in the case of the term ‘science’.