Sociology of science
- It aims to understand how knowledge is produced and validated
- It asks questions regarding the structure of power, hierarchies,
controversies, and alliances which underlie the work of science
The strong programme
Robert King Merton (July 4, 1910 – February 23, 2003) was an American sociologist. He spent most of his
career teaching at Columbia University. In 1994 Merton won the National Medal of Science for his
contributions to the field and for having founded the sociology of science
First sociology of science: study of how science is produced, and of scientific careers.
Differentiation for sociology of knowledge (Mannheim), which, in line with Marx and Engels,
claimed that people's ideologies, including their social and political beliefs and opinions, are
rooted in their class interests, and more broadly in the social and economic circumstances in
which they live. Study of ideologies and the social context in which things are believed to be
Merton studies researchers and scientific institutions within a structural functionalism
Studies of RECOGNITION: how scientists obtain scientific success.
Correlations between quantitative indicators:
• Number of publications (many scientists publish many articles of low significance, few
scientists produce few articles of high significance)
• Honorific awards and membership of honorific societies as forms of recognitions
• Positions in top ranked departments
• Number of citations
• Nobel prices
The goal is to verify that the system of rewards is justifiable: the scientific institution is
organised around a system of rewards whose FUNCTION is to confer recognition to the most
Science is organised in a scientific community with its own allocation system: the more
creative researchers are pushed to produce more, the less creative are relegated in
Recognition at an early start (University students who get a research assistant position in a
prestigious department). Late boomers are rare.
WEAK PROGRAM. This approach accept a logistic and objective definition of science: the
scientific method and the logic principle is not under discussion.
Macro approach: individual variables are aggregated and correlated.
It justifies science by justifying inequalities as a logic outcome of meritocracy: the reward
system rewards the best scientists an it is consistent along their careers
BIBLIOMETRY provides scientific administrators with a rational tool to evaluate the outputs of
science, with no attention to the quality of citations (eg: positive VS negative citations)
NO ATTENTION TO CONFILCTS
Thomas Samuel Kuhn (July 18, 1922 – June 17, 1996) was an American physicist, historian, and philosopher
of science whose controversial 1962 book The Structure of Scientific Revolutions was deeply influential in
both academic and popular circles, introducing the term "paradigm shift”.
Evolution of science is not a continuous process but a series of periods of normal science
followed by revolutions.
Science as a process of accumulation
• Science is shaped by paradigms: sets of shared beliefs among members of a speciality area,
where interests converge around a specific set of problems identified as significant for the
advancements of knowledge. By supporting the same paradigm, researchers also share
specific understandings of which research techniques are appropriate for investigating these
problems and a sense of identity that is constructed via interpersonal networks and the
processes of information sharing.
• Rules of scientific method do not correspond to what happens in practice: in normal science
scientists do not work to discover new theories, but a-critically accept the paradigmatic
ones, and work toward building empirical proof of them.
• The paradigm determines the questions that can be asked, and science develops as a world
independent from its social environment: AUTONOMY OF SCIENCE
Paradigms reach a point of depletion when there are no more interesting questions to explore.
Revolutions happen when scientists start focussing on errors of the established paradigm.
Thinking outside the box.
The revolutionary scientists are people who can manage a paradigm so well that they are also
able to overcome it.
The strong programme
Rationality, objectivity and truth are local sociocultural norms, shared conventions.
Scientific norms are like linguistic norms (Wittengstein): they are associated to specific
sociocultural groups, whose practices are regulated by conventions only valid for those
Social interests influence the content and development of science through tactics of
persuasions, strategic expedience, cultural dispositions. Scientific actions are not
determined by the nature of things or by logic possibilities, but by social factors.
Focus on interactions between scientists: facts are collective constructions
constituted within the interaction between who produces it and who receives it and
attempt to replicate it.
Falsifiability is an idealistic vision of the practice of science, where controversies are
not resolved via replications, but with negotiations of a core set of interested
Knorr-Cetina, Gilbert and Mulkay
Scientific objects are not only technically produced in laboratories, but also
constructed with literary skills of persuasion and with political strategies for making
alliances and mobilising resources.
Study of laboratory notes. Scientists do not normally mention unfavourable results,
and present the favourable ones with rhetorical strategies that give strength to what
they have found.
They give the impression that arts, creativity and intuition does not have anything to
do with production of science, pure result of the scientific method.
EMPIRICAL REPERTOIRE: typical of published papers, written in conformity with the empirical
representation of scientific action
CONTINGENT REPERTOIRE: practical skills, traditional tricks, informal dialogue within
scientists that does not follow the logical steps of scientific method.
Gilbert and Mulkay, Opening the Pandora’s box, 1984, p. 176
What he wrote (empirical repertoire)
What he meant (contingent repertoire)
It has long been known that
I haven’t bothered to look the reference
While it has not been possible to provide
definite answer to these questions
The experiment didn’t work out, but I
figured I could at least get a publication out
The W-PO system was chosen for detailed
The fellow in the next lab had someone
Accidentally strained during the mounting
Dropped on the floor
Handled with extreme care throughout the
Not dropped on the floor
Typical results are show
The best results are shown, i.e. those that fit
Agreement with the predicted curve is
ANT (Actor Network Theory)
Latour and Callon
Actor–network relates different elements together into a network so that they form an
apparently coherent whole. These networks are potentially transient, existing in a constant
making and re-making. This means that relations need to be repeatedly “performed” or the
network will dissolve. They also assume that networks of relations are not intrinsically
coherent, and may indeed contain conflicts. Social relations are only ever in process, and must
be performed continuously.
Actants denote human and non-human actors, and in a network take the shape that they do
by virtue of their relations with one another. It assumes that nothing lies outside the network
of relations. As soon as an actor engages with an actor-network it too is caught up in the web
A car is an example of a complex system. It contains many electronic and mechanical
components, all of which are essentially hidden from view to the driver, who simply deals with
the car as a single object. This effect is known as punctualisation.
When an actor network breaks down, the punctualisation effect tends to cease as well. In the
automobile example, a non-working engine would cause the driver to become aware of the
car as a collection of parts rather than just a vehicle capable of transporting him or her from
place to place.
Depunctualization is like the opening of a black box. When closed, the box is perceived simply
as a box, although when it is opened all elements inside it becomes visible.
Attention to the structural possibilities and constraints that shape scientific
The field of science is a social field of strengths with its own structure and battles to
conserve or change the hierarchical structure that is produced by the field itself.
Scientists build up the field through their relationships, which means that the field
structure is generated by actors' relationships. The possibility, for every actor to
deform the field depends on his/her weight, which consist in the relative amount of
symbolic capital which everyone owns.
The symbolic capital is “the form that one or another of capital species [economic,
cultural and social capital] takes when it is grasped through categories of perception
that recognize its specific logic or, if you prefer, misrecognize the arbitrariness of its
possession and accumulation”.
This symbolic capital assumes a special feature in the scientific field and it is called
scientific capital, made by connaissance and reconnaissance.
Victory is measured in terms of the amount of scientific capital attributed to each
scientist (and the institutions to which he or she belongs) by his or her peers.
Therefore the stake is internal to the field and grants its autonomy from other social
Elements of analysis
Methods of construction of
scientific object is not the object of
sociology of science
Focus on controversies,
communities boundaries defined
by sets of problems
Local socio-cultural norms
Focus on local interactions rather
than on structures and
Scientific objects as cultural
Regularization in an empirical
repertoire is a local and individual
strategy to gain prestige.
Networks of human and non humans
No empirical detection of networks
Lock in effects of networks are
Individual variables (combination of
capitals) are abstracted to form
position in the social structure
No empirical detection of networks
of concrete relationships
Network approaches to the study of science
• Not limited to the micro level of local interactional mechanisms, and not jumping
from the micro to the macro abstracting individual features: SNA focuses on
• Relationships between scientists cannot be reduced to co-authorship and cocitations, but involve a wider set of interactions, from competition for research
funding to affiliation to different organizations
1. PATH ANALYSIS
2. MULTILEVEL NETWORKS
Focus on empirical connections to test the robustness of various theories in
sociology of science and to study the evolution of several disciplines
Diana Crane, 1969, Social Structure in a Group of
Scientists: A Test of the "Invisible College“, American
Sociological Review, Vol. 34, No. 3, pp. 335-352
The existence of "invisible colleges" has been difficult to prove:
• The boundaries of research areas are difficult to define since most scientific
work can be classified in numerous ways.
• Scientists have many contacts with other scientists in their own research
areas and in other fields, some fleeting, some lasting.
If social organization exists in a research area, it is of a highly elusive and
relatively unstructured variety.
The existence of social organization within a research area may be inferred
(a) if scientists who have published in the area have more social ties with one
another than with scientists who have not published
(b) if scientists who have published in the area can be differentiated by degree
of social participation within the area.
Informal communication regarding research findings, research-in-progress, and
research techniques represents one way in which members of a problem area can
be linked to one another.
- information about informal communication was obtained only from those
currently engaged in research in the field, a subsample of 52 members from a
total of 147 respondents.
In addition to informal communication, several other types of ties between
scientists exist. Collaboration occurs in several ways.
- In the case of formal collaboration communication between two or more
scientists about their research was so important that it received formal
recognition in the publication itself.
- Another form of collaboration takes place when a student writes a thesis under
the direction of one or more teacher
- intellectual linkages represented by the influence of one scientist's work upon
that of another. Citation references in journal articles are direct indications of
such influence (can also be measured by asking scientists to name others who
have influenced them in their selection of problems and techniques )
NB: many articles include numerous citations, but the relative importance of each
citation may vary consider-ably from a reference to a scientist whose work has had
a very strong influence on the author to a scientist whose work is relevant only in
connection with a minor point
Combining several indicators into a composite index supplies further information about
Some members may be related to other members through influences on the selection of
problems or techniques, others through some type of collaboration or through informal
communication, but, if social organization does exist in a research area, most members
should be related to others in at least one of these ways.
The choices of members of a group are arranged in matrix form where one axis represents
choices made by a member of the group; the other axis, choices received by a member
• Degree (indegree and outdegree)
• Analysis of subgroups:
Members of the problem area were divided into five groups on the basis of productivity
8 High Producers, each of whom had published more than ten papers in the area
11 Moderate Producers, who had published four to ten papers in the area;
33 Aspirants, who had published fewer than four papers in the area.
9Defectors, each of whom had published four to ten papers in the area
86 Transients, each of whom had published fewer than four papers in the area
At least four methods of locating members of a problem area are possible: the use of
bibliographies, abstracting services, citation networks and sociometric data.
Selected scientific field:
The diffusion of agricultural innovation
Questionnaires were sent to 172 of the 221 scientists listed,10 both junior and senior
authors; 147 replies were received. Each respondent was sent a letter which included
references to his publications as listed in the bibliography and was requested to respond to
the questionnaire with respect to those publications only
Out of a total 1351 choices made by all respondents on all the different types of ties,
outsiders were named 684 times (51%) and problem area members 667 times (49%).
Does this suggest that a social group within the area did not exist?
the majority of "outsiders" were selected only once.
Within the problem area, about half the members were never named, but most of these
scientists had been relatively unproductive.
On the other hand, 26, or 27% were named more than five times. 15, or 7%, were named
more than ten times. Thus, the social organization of the problem area appeared to be
centered around a small and relatively productive proportion of the total membership.
Respondents were asked to indicate if they had any personal acquaintance with the
scientists whom they mentioned as having influenced their selection of problems in the
• of 246 choices of problem area members, 76% were designated as personal
• of 256 choices of outsiders, 57% were indicated to be personal acquaintances
This outcome suggests that to some extent the influence of outsiders was exerted through
publications, while that of insiders was exerted through personal contact.
• During the first ten years of activity in the area, only 5 % of the members were active.
• Between 1948 and 1958, the number of authors entering the area doubled every three
• After 1958, the number of authors entering the field doubled every five years
• The increase per year stabilized at approximately 17 authors per year.
The trend for the number of publications was similar.
• Between 1951 and 1960, 49% of the authors entered the field, most of this increase
occurring during the latter part of the decade.
• Another 46% entered between 1961 and 1966.
Trends for high producers:
• Two High Producers entered the field in the first decade
• In the middle of the second decade, each of these High Producers had a student who
• also became a High Producer. Another thesis director and his student, both High
Producers, entered the field at the same time along with a collaborator who also
• High Producer.
• By 1957, all of the High Producers had entered the field.
• Although almost two-thirds of the Aspirants and more than one-third of the Transients
entered after 1960, only 11 % of the more productive scientists (those with more than
three publications in the area) entered the field in that period.
On the basis of collaboration and student-thesis director relationships, members of the area
were assigned to distinct subgroups of varying sizes
A scientist was assigned to a particular group of collaborators if he had a published
Collaboration with at least one of its members or had been the student or thesis director of
at least one of its members.
• Before 1956, the group consisted of small groups of collaborators and student-teacher
pairs and a number of isolates.
• After 1956, when the group as a whole increased in size, some of these small groups
expanded. Two large groups emerged, with 27 and 32 members respectively, as well as
several medium-size groups with five to 13 members. A number of new small groups with
2 to 4 members and numerous isolates appeared
It seems plausible that these large groups could have exercised control over the direction of
research in the area. Since their approach to the field was so visible due to the large number
of publications produced by these groups, scientists outside these groups who had other
types of approaches might have found it difficult to exert a comparable influence
In this tradition, many network studies
• Burt, R.S., 1978/79. Stratification and prestige among elite experts in methodological and
mathematical sociology circa 1975. Social Networks 1, 105–158.
• Hummon, N.P., Carley, K., 1993. Social networks as normal science. Social Networks 15, 71–
• Hummon, N.P., Doreian, P., 1989. Connectivity in a citation network: the development of
DNA theory. Social Networks 11, 39–63.
• Liberman, S., Wolf, K.B., 1998. Bonding number in scientific disciplines. Social Net-works 20,
• Lievrouw, L.A., Rogers, E.M., Lowe, C.U., Nadel, E., 1987. Triangulation as a research
strategy for identifying invisible colleges among biomedical scientists. Social Networks 9,
Lazega et al. (2008): multi-level networks of two systems of superposed
and partially interlocked interdependencies, one inter-organizational, the other
Combination of centrality measures at the individual level and at the
organizational level: FISHES AND PONDS
Important distinction in the conceptualization of micro and macro levels.
Micro level: the network of individual interactions, together with attributes.
Macro level: the network of inter and intra ties within and between
Meso level links the two together via bipartite networks, accounting for
interdependencies through matrix algebra.
Bellotti E., 2012, Getting funded. Multi-level
network of Physicists in Italy, Social Networks, 34:
- 10 bipartite networks of “people by projects”, one for each year from 1997 to 2006 summed
up to observe overlap
- University affiliation: “people by university” matrix obtained by attribute
- “people by projects” multiplied by “university by people” = “university by projects”
- “people by people” valued network of number of projects in common,
and the equivalent valued network for Universities. Both networks are undirected
3116 physicists working in 73 Universities; 1122 of them, who work in 66 Universities, have
been funded during the 10 years under analysis
- University affiliation
- Individual affiliation to physics sub-disciplines (experimental physics, theoretical and
mathematical physics, material physics, nuclear and subnuclear physics, astronomy and
astrophysics, earth system physics, applied physics, and history and didactic of physics) +
scientists from other areas = interdisciplinarity of projects
- Rank of scientists (full professor, associate professor, and researcher)
- National coordinators
DEPENDENT VARIABLE: TOTAL AMOUNT OF FUNDING RECEIVED BY EACH SCIENTIST
OVER THE 10 YEARS
Analysis of attributes
8 people (0.7%) have been national coordinators 4 times, 29 people
(2.5%) 3 times, 64 people (5.7%) 2 times, 215 people (19.1%) 1 time,
and 806 people (72%) have never been national coordinators.
H1. On average, national
coordinators are more likely
to obtain a larger amount of
money for research than
researchers who never lead
a research group.
H2. On average, full
professors are more likely to
obtain a larger amount of
money for research than
History and didactic
H3. On average, researchers
working on experimental
physics, astronomy, and
material physics, and in other
disciplines, are more likely to
obtain a larger amount of
money than researchers
working in other sub-disciplines.
Analysis of the micro level of collaborations
H4. On average, researchers with
higher E-I index values for subdisciplines are more likely to
obtain a larger amount of money
for research than the ones with
Two strategies of getting
connected: brokerage VS closure
H5. On average, researchers
with higher ego brokerage scores
and/or higher egonetwork density
are more likely to obtain a larger
amount of money for research
than researcher with low values
in one or both measures.
Analysis of the macro level of collaborations
OssA strofA rcetri
Cattolica IA SF
H6. On average, researchers working in core institutions are more likely to obtain a larger
amount of money for research than researcher working in peripheral ones.
Analysis of the meso level of collaborations
Big fishes: scientists with a valued degree above the median = 5. 535 BF, 587 LF
Big ponds: Universities with a number of appointed physicists above the mean = 42. 32 BP, 34 LP
Fish and Pond
H7. On average, big
fishes in big ponds
(BFBP) are more likely to
obtain a larger amount
of money for research
than little fishes in big
ponds (LFBP), big fishes
in little ponds (BFLP) and
little fishes in little
Modelling funding achievements*
* checked by J. Koskinen using Network Disturbances model to correct for potential lack of
independence between cases, following Leenders (2002)
Being a national coordinator is significant, and on average it increases the
amount of funding
Rank is significant: full professors are on average more funded than others
H3 not confirmed
Working in experimental physics is significant, but it does not increase the
average amount of money obtained by full professors.
Working in applied physics, history and didactic, and other disciplines
significantly decreases the amount of funding
H4 not confirmed
E-I index calculated for disciplines is not significant
Brokerage is highly significant.
Closure also has a positive impact on the average amount of funding, even if
not as strong as brokerage, but the effect is positive only when brokerage and
closure are included together in the model, closure alone not being significant.
Brokerage strategy does not work for astronomers: the values for their
discipline become negatively significant when brokerage values are taken into
account. It also diminishes the gap between full professors and other
H6 not confirmed
Working in a core institutions is not significant
Little fishes are generally penalized against the big fishes, but little fishes
in big ponds are more penalized than little fishes in little ponds.
The meso level variables diminish the positive effect of brokerage
Several approaches in sociology of science
The strong programme
Two main approaches in SNA
• Path analysis
• Multilevel networks