=Paper=
{{Paper
|id=Vol-3834/paper39
|storemode=property
|title=Epistemic Capture through Specialization in Post-World War II Parliamentary Debate
|pdfUrl=https://ceur-ws.org/Vol-3834/paper39.pdf
|volume=Vol-3834
|authors=Ruben Ros,Melvin Wevers
|dblpUrl=https://dblp.org/rec/conf/chr/RosW24
}}
==Epistemic Capture through Specialization in Post-World War II Parliamentary Debate==
https://ceur-ws.org/Vol-3834/paper39.pdf
Epistemic Capture Through Specialization in
Post-World War II Parliamentary Debate
Ruben Ros1,∗,† , Melvin Wevers2,†
1
Institute of History, Universiteit Leiden, Leiden, The Netherlands
2
Department of History, Universiteit van Amsterdam, Amsterdam, The Netherlands
Abstract
This study examines specialization in Dutch Lower House debates between 1945 and 1994. We study
how specialization translates in the phenomenon of “epistemic capture” in democratic politics. We com-
bine topic modeling, network analysis and community detection to complement lexical “distant read-
ing” approaches the history of political ideas with a network-based analysis that illuminates political-
intellectual processes. We demonstrate how the breadth of political debate declines as its specialist
depth increases. To study this transformation, we take a multi-level approach. At the (institutional)
macro-level, network modularity shows an increase in the modularity of topic linkage networks, indi-
cating growing specialization post-1960, linked to institutional reforms. At the (political) meso-level, we
similarly observe specialization in node neighborhood stability, but also variation as the consequence
of ideological and party political change. Lastly, micro-level analysis reveals persistent thematic com-
munities tied to increasingly stable groups of individuals, revealing how policy domains and politicians
are captured in ossified specialisms. As such, this study provides new insights into the development of
twentieth-century political debate and emergent tensions between pluralism and specialism.
Keywords
Parliamentary Discourse, Epistemic Capture, Temporal Networks, Topic Linkage, Specialization
1. Introduction
Specialization, Democracy, and Epistemic Capture
Specialization is a hallmark of capitalist modernity. The division of labor into specialisms has
propelled efÏciency and productivity in economic, administrative, and scientific contexts [10].
Politics is no exception. In the past decades, decision-making processes have been delegated
to specialist experts, and politicians increasingly operate as specialists [37]. In the context of
highly technical policy challenges, they can no longer afford to be generalists. While such
specialization can enhance depth of knowledge and decision-making efÏciency, it also harbors
intrinsic risks within democratic politics, as thinkers from John Dewey to Jürgen Habermas
have pointed out [9, 13]. The worldview of specialists and politicians may differ substantially
from that of citizens. In fact, many scholars consider recent populist upsurges a response to
a growing dominance of a class of specialized experts [30]. This signals a more fundamental
CHR 2024: Computational Humanities Research Conference, December 4–6, 2024, Aarhus, Denmark
∗∗
Corresponding author.
£ r.s.ros@hum.leidenuniv.nl (R. Ros); melvin.wevers@uva.nl (M. Wevers)
ç https://rubenros.nl/ (R. Ros); https://www.melvinwevers.nl/ (M. Wevers)
ȉ 0000-0002-0877-7063 (R. Ros); 0000-0001-7116-9338 (M. Wevers)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
409
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ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
tension: the process of specialization is driven by a search for efÏciency and productivity. Spe-
cialists value skill and “truth”, whereas democratic politics, revolves around conflicting view-
points, popular sovereignty, and regular debate. As an “empty space”, it defies desiderata of
efÏciency and truth [18, 35]. Specialization and democracy, in other words, are in tension [36].
The specific risk posed to democracy by specialization can be called epistemic capture [39].
Such capture manifests when an individual’s or group’s specialized expertise in a specific do-
main results in a constrained perspective that dominates their analytical and decision-making
processes and is taken by non-specialists as “truth”[12]. This narrowed focus can act as an in-
tellectual filter, obscuring the broader context and impeding deliberation over diverse insights
and alternative approaches. Consequently, this expertise—induced myopia can significantly
hinder comprehensive problem—solving and balanced policy formulation [5]. The democratic
debate is thus “captured” by specialists.
Specialization and the History of Ideas
These captive effects of specialization also matter to the historical study of ideas. Intellectual
historians commonly focus on the eighteenth and nineteenth centuries as eras of intellectual
creativity, of nascent ideologies and emergent public spheres. The twentieth century, as some
historians observe, appears markedly different. As the previously fertile soil for intellectual
and ideological evolution, political debate became arid through specialization and “scientifi-
cation” [11]. Historians are only at the start of understanding these structural changes in
twentieth-century political debate. In this challenge, they face the enormous scale and com-
plexity of twentieth-century intellectual production and circulation, one that is hard to grasp
with traditional “close reading” methods. It is hard to imagine how a survey of lexicons or
even exploratory keyword analysis would probe into a structural transformations such as spe-
cialization. Nevertheless, understanding specialization through the lens of language and con-
cepts appears a crucial complement to prevailing institutional approaches to the emergence
of specialists. Politicial scientist have closely studied specialist bodies such as parliamentary
committees. Yet, the “informal” and “epistemic” aspects—crucial for understanding the impact
of specialization on political debate—are hard to grasp through the lens of so-called “distribu-
tive” approaches, that consider specialization primarily as politics by other means[31]. At
the same time, political scientists have long recognized the importance of specialists in inter-
preting reality for policy-makers at large [7]. Specialist communities have come to dominate
policy-making in their area of expertise. In the Dutch case, the “Green Front” (a community of
agricultural specialists) is a known example of such a political and epistemic hegemony [16].
These examples invite us to study specialization beyond procedural reform and inquire into
the epistemic capture of policy areas by specialist perspectives.
New Computational Approaches to Studying Ideas
Historians have long endeavored to reconstruct the web of ideas, worldviews, and cognitive
constructs that have shaped human culture throughout history. Traditional methods, such
as close reading and contextualization of texts drawn from archival research, have provided
invaluable insights. However, these methods have always struggled with the scale of the his-
410
torical record. Especially in democratic societies, where ideas develop through public debate,
it is difÏcult for historians to grasp their complex circulation, production, and reception. In
recent years, computational analysis has opened up new avenues for historians studying ideas
and worldviews at scale. By applying algorithms and statistical techniques to large corpora
of historical texts, researchers can discern patterns and trends that may be invisible to the
human eye, using language-use as a reflection of collectively held ideas [6, 20]. These “dis-
tant reading” methods—computational approaches to analyzing large volumes of text—often
rely on keywords and their distribution over various contexts [24]. While these methods offer
new insights, they also have limitations. Distant reading tends to revolve around singular key-
words and ideas, with historians taking the former as an index to the latter. These words are
usually explored through frequencies, collocations, and distributional vectors [34]. While this
mode of lexical exploration boosts the efÏciency of the research process, its impact on the way
historians study and assess the history of ideas is limited.
Recently, scholars have begun employing more sophisticated methods to transcend the study
of individual concepts and ideas. By prioritizing theory-driven modeling over lexical explo-
ration, they have tackled questions and concepts in the history of ideas that were long regarded
too complex or big to empirically study. For instance, Ryan Heuser tests several long-standing
hypotheses regarding general conceptual transformations during the “Sattelzeit” (saddle time),
a transitional era between early modern and modern history [14]. Recent work by Vicinanza
et al.[38] shows how ideas tend to emerge in peripheral spaces, illustrating how various stud-
ies have recently taken information-theoretic measures to quantify the novelty and resonance
of intellectual developments. These approaches differ from “distant reading” by focusing on
structures and processes [28]. Instead of merely analyzing word distributions, they utilize low-
dimensional numerical representations of texts to explain complex dynamics. This allows these
scholars to make more novel and ambitious claims about the production, circulation, and con-
testation of ideas in the past. In this paper, we build on this new direction in computational
intellectual history, by looking at specialization as an intellectual and epistemic process.
The Dutch Lower House: A Case Study in Specialization
The Dutch Lower House exemplifies the trend toward specialization in the twentieth century.
Gradually, members of parliament came to believe that specialist deliberation was more efÏ-
cient and productive. This marked a significant departure from the nineteenth century, when
specialism was often equated with narrow-mindedness [15]. During the interwar period, how-
ever, politicians observed a widening gap between the expert bureaucracies of the expanding
state and parliamentary generalists. Consequently, parliamentary specialists emerged in spe-
cific domains such as foreign policy and agriculture. After the Second World War, this trend
gained an institutional footing. Similar to other Western European contexts, so-called perma-
nent committees were established in which specialists could wage a more technical debate [32,
2]. The Dutch multiparty system and the desire of the main parties to depoliticize sensitive
issues only stimulated such specialization [19]. Previously, parliament had met in randomly
allocated subgroups, known as “departments”, reflecting the generalist assumptions of the time.
The new Rules of Order drafted in 1966, a decade after the experimental introduction of per-
manent committees, abolished the departments [15]. Since then the Dutch Lower House has
411
grown into a legislative institution that is marked by high levels of specialization [1].
Given this historical context and the broader implications of specialization in democratic
institutions, our study aims to address the following research question:
1. How did specialization in the Dutch Lower House evolve between 1945 and 1995
2. To what extent did it lead to epistemic capture in parliamentary debates?
To answer these questions, we employ a novel computational approach that combines topic
modeling, network analysis, and dynamic community detection. This method allows us to trace
the evolution of specialized knowledge domains in the Dutch Lower House over five decades,
offering insights into the process of specialization and its effects on parliamentary discourse.
By applying a dynamic network analysis to links between topics in debates, we aim to con-
tribute to a deeper understanding of how ideas and cognitive constructs shape political realities.
This study also seeks to shed light on the tensions between specialization, pluralism, and con-
tingency in democratic decision-making, offering a new perspective on the challenges facing
modern democracies.
2. Data
2.1. Parliamentary Proceedings
This study utilizes the digitized parliamentary proceedings of the Dutch Lower House from
1946 to 1995. The proceedings consist of speeches held in the Lower House that are transcribed,
edited, and published. In recent years, the proceedings have been digitized and linked with
metadata on speakers, parties, and dates. The quality of the Optical Character Recognition is
known to improve considerably for postwar proceedings [17]. To prepare the data for analysis,
we conducted several preprocessing steps. First, we removed stop words to eliminate common
but uninformative words. Next, we filtered out speeches shorter than ten words, as these are
unlikely to contain substantial content. We also limited our analysis to nouns, verbs, adjectives,
and adverbs, as these parts of speech are most informative for semantic analysis.
In addition to text preprocessing, we also excluded several types of speeches. First, we re-
strict our analysis to plenary debates. With the omission of committee meetings (also present
in our data), we prevent our models from reflecting merely committee language, instead forcing
them to measure the degree of specialization in plenary debates. Second, we excluded speeches
by the House chair, which often contain procedural and repetitive language not directly rele-
vant to substantive debates. Including these speeches could disproportionately affect the topic
model and subsequent linkage scores, introducing noise and potentially obscuring meaningful
patterns in the data. Thus, excluding the chair’s speeches serves both conceptual and method-
ological purposes, ensuring our analysis focuses on the most informative and relevant content.
Our data comprises 52,396,073 tokens and 495,053 types. The data size varies unevenly over
the period; after 1967, the size of the parliamentary text gradually expands. Whereas an average
year in the 1950s contains 1.25% of the tokens, this number rises to 4% in 1979.
412
3. Methods
Our methodological approach consists of four main steps: 1) pre-processing of parliamentary
proceedings, 2) topic modeling to identify thematic content, 3) network analysis to map re-
lationships between topics, and 4) dynamic community detection to identify clusters of spe-
cialized domains (see Figure 1). This multi-step process allows us to trace the evolution of
specialized knowledge domains in the Dutch Lower House over five decades.
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1. Prepare Proceedings 2. Generate Topic Distributions 3. Create Linkage Networks 4. Identify Paths and Members
Figure 1: Example topic linkage network (top) and methodological workflow (bottom).
3.1. Topic Models
To examine specialization as an epistemic phenomenon, we analyzed the dynamic structure of
the content of parliamentary debates using topic modeling techniques. We employed Latent
Dirichlet Allocation (LDA) using the Mallet toolkit to generate low-dimensional representa-
tions of the semantic content of the speeches [3, 21].1 We chose LDA over more recent tech-
niques such as top2vec and BERTopic because the vector-based similarity scores of the latter
yielded relatively unclear linkage networks. Specifically, we configured the LDA model with
250 topics to capture a wide range of potential themes in the parliamentary debates. Each
speech was segmented into 50-word chunks to account for the declining speech lengths over
the study period. After training, we averaged the topic distributions for each member within
a single session to facilitate subsequent network analysis.
The trained topic model captured both thematic categories aligned with policy areas and
procedural and rhetorical categories, such as filing motions or citing newspapers. We chose
1
Latent Dirichlet Allocation (LDA) is a probabilistic model that assumes documents are mixtures of topics, where a
topic is a probability distribution over words.
413
250 initial topics to capture a wide range of potential themes in the parliamentary debates.
(Appendix Topic Models). After manual inspection, we identified and removed 116 topics that
primarily represented procedural or rhetorical aspects of the debates rather than substantive
policy areas based on domain knowledge. This reduction allows us to focus our analysis on
the thematic content most relevant to our research questions about specialization in policy
domains. We then (re)normalized the remaining topic distributions to ensure comparability
across sessions. We consider this filtering justified because it forces our network-based ap-
proach to focus on connections between themes. Also, the metrics we use in the subsequent
analysis appear robust to this filtering.
3.2. Networks and Communities
To quantify the connectedness of parliamentary speech topics, we leverage topic linkage scores
based on Pointwise Mutual Information (PMI), building on the work by Perry and DeDeo [27]
(See Appendix Linkage function). PMI measures the degree of co-occurrence of topics in a pe-
riod beyond random chance, with higher scores indicating stronger associations between top-
ics. We calculate PMI scores between pairs of topics and use this to create network structures of
topics that have a PMI higher than 0 (See Figure 1 as an example). Network metrics enable us to
investigate the evolving architecture of linkages over time. To address PMI’s sensitivity to rare
topics, we employ a smoothing function based on joint frequency [26]. This approach prevents
low-frequency topics from becoming highly connected and central in the networks, resulting
in more accurate representations of topical connections. We construct PMI-based networks on
time periods of 6 months, acknowledging changes across parliamentary years and differences
between budgetary debates (that commonly dominate August-December) and other types of
debates between January and July [27].
After generating the networks, we applied the Louvain community detection algorithm to
identify clusters of related topics [4]. This algorithm optimizes modularity, a measure of the
density of links inside communities compared to links between communities, potentially re-
flecting the emergence of specialized epistemic domains within parliamentary debates. We
tested multiple resolution parameters, which control the size and number of detected commu-
nities, and found that a resolution of 3 provided the most meaningful and interpretable results
based on our domain knowledge of Dutch parliamentary history. This choice was robust across
different time periods and yielded communities that align well with known policy domains and
historical developments in Dutch politics.
To analyze the properties and evolution of these topic networks and communities, we rely
on several key metrics.
1. Network Metrics (Modularity, Network Density, Node Degree). Modularity assesses
the degree of community structure within a network [25]. High modularity values in-
dicate that the network has dense connections between nodes within communities and
sparse connections between nodes in different communities, suggesting a strong parti-
tion into specialized domains. Network Density quantifies the overall “connectedness” of
a network, calculated as the ratio of the actual number of edges to the maximum possible
number of edges [23]. Node Degree, conversely, measures the “connectedness” of a sin-
414
gle node by counting the number of edges attached. These metrics help identify highly
connected topics that may play central roles in structuring parliamentary debates.
2. Clustering Metrics (Normalized Mutual Information (NMI)). To evaluate the similarity
between clusterings in different periods we use Normalized Mutual Information. This
metric calculates the mutual information between two distributions of nodes over com-
munities and provides a normalized score between 0 and 1 that quantifies the degree of
mutual information [22].
3. Set Similarity Metrics (Overlap CoefÏcient). To compare node neighborhoods and
track the evolution of communities over time, we employ the overlap coefÏcient to cal-
culate the similarity (node neighborhood similarity) between two sets [33]. This mea-
sure quantifies the size of the intersection between two groups relative to the size of the
smaller group, highlighting the extent to which the smaller group is contained within
the larger one. This makes the overlap coefÏcient suitable for quantifying “epistemic
capture”, more so than the common Jaccard Similarity. Because the latter compares the
size of the set intersection to the size of the set union, it neglects the possibility that a
set is fully present in another. The overlap coefÏcient is particularly useful for detecting
significant overlap when one group is substantially smaller than the other, a common
scenario in longitudinal studies where group sizes fluctuate. By calculating the overlap
coefÏcient between neighbors in period 𝑃 and neighbors in period 𝑃 −1, and aggregating
these stability scores, a coarse picture of stability can be mapped.
4. Community Paths. To capture the formation and change of communities over time,
we employed the CDlib (Community Discovery Library) to perform temporal commu-
nity analysis. Using the Temporal Network Clustering algorithm, we match commu-
nities in different time periods [29]. Specifically, we use the aforementioned Overlap
CoefÏcient to calculate the similarity of communities based on their topic composition.
The algorithm links communities across adjacent time steps based on node overlap, pro-
viding insights into the stability and dynamics of specialized knowledge domains in the
Dutch Lower House. This analysis reveals persistent topic clusters and volatile areas,
potentially reflecting changes in political priorities, the emergence of new issues, or the
restructuring of existing policy domains.
4. Results
Instead of using single (information-theoretical) metrics to measure specialization, we employ
topic linkage networks to enable a multi-level analysis of specialization in parliamentary de-
bates. Global network statistics reveal long-term developments, while similarities between
communities, topics (nodes), and linkages (edges) point to local dynamics and contextual fac-
tors. We identify specialization and its effects on the macro, meso, and micro-level. Through
specific metrics and signals at each level, we integrate different explanations, allowing us to
differentiate between various causal forces and contextual factors that shape specialization.
415
(a) Modularity (black solid line) and density
(grey dashed line) of temporal linkage net-
works. Vertical grey lines indicate cabinet (b) Clustering Stability as measured through
changes Normalized Mutual Information (NMI).[8]
Figure 2: Macro-Level Specialization. Vertical dashed lines indicate cabinet changes.
4.1. Macro-Level Analysis: The Emergence of Specialized Communities
Our analysis starts with an attempt to gain a macro-level perspective on the extent of special-
ization in the Lower House. We expect a gradual increase in modularity—the extent to which
networks exhibit community structure—to reflect this specialist compartmentalization of de-
bates. Indeed, temporal networks show a notable increase in modularity between 1950 and
1975, followed by stabilization that persists until the 1990s. The initial increase in modularity
contrasts with a declining network density (the overall “connectedness” of a network). The
divergence between modularity and density trends (Figure 2a) suggests that while topics are
becoming more specialized (increasing modularity), they are also becoming more isolated from
other topics (decreasing density).
Modularity and density thus point at increased community structure. However, special-
ization would not only entail community structure, but also a crystallization or stabilization
of communities. To assess the extent of stabilization, we measure stability using Normalized
Mutual Information (NMI), which quantifies the similarity between clusterings from different
periods, with higher NMI values indicating greater stability. From 1946 to 1994, we calculate
NMI scores for each pair of consecutive six-month periods. The upward trend (Figure 2b) and
considerable increase (from about .5 to .6) in these scores shows that topic clusters—specialized
communities—are becoming increasingly stable over time. This indicates that specialized top-
ics in parliamentary debates are not only forming but also becoming more entrenched, reflect-
ing a growing stability in how topics are grouped and discussed.
The extent and stability of community structure as measured above suggest the gradual
advance of specialization in the Lower House. The signals roughly correspond to the known
solidification of the committee system in the 1950s and 1960s. In this period, these institutions
became the focal actors in parliament. Our analysis shows, however, that this institutional
advance also left an imprint on the way politicians thought and talked: the breadth of debate
declined, while its depth increased.
416
4.2. Meso-Level Analysis: Specialist Communities between Politics and
Procedures
The macro-level analysis confirms the gradual specialization of the Lower House, especially in
the decades between 1950 and 1975. However, they say little about driving factors and local
dynamics that are clearly present in the previous figures. To investigate specialization beyond
linear increase, we take a meso-level look at the specific nodes in the networks. We begin with
studying the changing neighbors of nodes. Overall, node neighborhood similarity increases
over time, confirming the macro-level trend of specialization (see Figure 3). The overlap coef-
ficient, which measures the similarity of a node’s neighbors between periods, shows a slight
upward trend after a decline around 1965. This suggests that topics tend to maintain their asso-
ciations over time, reinforcing the stability of specialized communities. Cabinet changes appear
to influence neighborhood stability, with sharp declines followed by gradual recovery during a
cabinet’s tenure. This pattern highlights the interplay between political change and epistemic
shifts in parliamentary discourse, suggesting that—amid general institutional change—political
changes also manifest as epistemic changes. The reshufÒe in committee membership at the
start of a cabinet period translates into a epistemic “reset” followed by gradual crystallizations
of networks. If specialist communities would persist without any sensitivity to political change,
the variation would not be visible in the figure.
The interplay of (institutional) macro-level specialization and (political) meso-level dynam-
ics can be illustrated by looking at individual topic neighborhood change (see the three figures
at the top in Figure 3). First, to the right, the topic of “broadcasters” shows low overall neighbor-
hood stability. This signifies the role of public broadcasting as a volatile topic, often involved
in disparate political conflicts. By contrast, the topic of international conflict shows high over-
all stability. Foreign affairs was the domain of specialists who forced the topic into a fixed
epistemic mold. Neighborhood stability did fluctuate, likely due to different international con-
flicts, but overall, stability remained high. Lastly, the topic of inflation shows another motif.
Stability declines from 1955 onward, as uncertainty grew about surging inflation rates and the
best way of combating them. After 1966, stability increases gradually until 1982, reflecting
a consolidating perspective on inflation. The breakthrough of this (neoliberal) interpretation
of inflation, and the subsequently drawn links between inflation and a plethora of other is-
sues, demonstrates as a sharp drop in 1982. As such, these three topics show how, despite
the overall rise of specialization, events and conflicts produced variation in the stability of a
topic’s neighborhood. In other words, the epistemic capture induced by specialization is de-
pendent on political events and ideological change. Topics could be epistemically captured,
but also break free from solidified structures through political contestation. This means that
macro-level epistemic capture was no teleological force, but rather a gradual transformation
that allowed substantial variation driven by distinct political factors.
4.3. Micro-Level Specialization: Linking Epistemic Structures to Political
Reality
Specialization and its captive consequences can be further contextualized and understood
by looking at specialist communities. By connecting communities in different time periods,
417
Figure 3: Topic Neighborhood Similarity (TNS). TNS measures the similarity between the neighbor-
hood of a topic (node) in a period and its 𝑁 preceding period as measured with the overall coefficient.
The bottom figure shows the average similarity for all nodes. The top figures show the similarity scores
for three specific nodes. Vertical dashed lines indicate cabinet changes.
“paths” of specialist communities can be traced. These paths consist of communities, that, in
turn, consists of topics. In Appendix Community Paths, we show how the paths begin, dissolve,
or persist. We highlighted specialist community paths that relate to foreign policy, education,
and agriculture: the most specialized areas.2
Paths of specialist communities shed yet another light on postwar specialization and epis-
temic capture. They offer the possibility to study specialization not only as intensification—in
the form of an increasingly focused debate following procedures and party politics, but also as
the epistemic capture of new topics and communities, integrating them in existing specialist
chains. The overview of the chains (Appendix Community Paths) points to moments where
multiple chains were born, and where the majority of communities fell within a chain. Three
moments, in particular, stand out. First, the late 1960s appear as a moment where several
chains emerged. New issues, such as public housing, municipal reorganization, wage policy,
and social benefits are captured in communities that persist into the early 1970s. This aligns
with historiographical depictions of the era as one of renewed labor militancy. Second, the
“captivity” of topics peaks again around 1980. The zenith of new polarization in the Lower
House, the turn of the decade forms the stage for emergent specialist chains, around constitu-
tional reform, nuclear waste, and business subsidies. The third and final peak occurs around
1990. The salience of social benefits, organized consultation, and public transport reflects in
2
We calculated this as the number of periods the same topic occurs in a chain, which yields a topic-level score that
indicates the persistence of a topic in a path.
418
new chains, reflecting the political agenda.
Chains of connected communities signify relatively stable specialisms. However, the ques-
tion remains how these epistemic structures bear a footing in the daily work of individual
politicians. To analyze this dimension, we turn to the relationship between politicians and
topics. Politician-topic interaction reveals the connection between epistemic structures and
political reality. By calculating the conditional probability 𝑃(𝑡𝑜𝑝𝑖𝑐, 𝑚𝑒𝑚𝑏𝑒𝑟) in each period,
we identify specialists connected to chained specialisms. Specifically, we z-score normalize
the conditional probabilities and assign politicians to communities based on topics where the
probability is larger than 1.
The connections between politicians and specialist communities can be used to validate the
linkage networks as grounded in parliamentary practice. Filtering communities with strong
connections to specific members points to, for example, financial specialists such as Anton
Lucas or agricultural specialists such as Jur Mellema. Generally, the predictability of topics—
expressed through entropy—in a specialist chain closely correlates with the predictability of
associated politicians. Figure 4a shows this correlation: paths with predictable (or stable) topics
also have predictable members. Paths with unpredictable topics (corresponding to unstable
specialisms) have unpredictable members. This shows that the epistemic dimension measured
through topic linkage corresponds to the actorial dimension of parliamentary work.
Path-politician connections also demonstrate how the politicians that link to specialist paths
also tend to stabilize. Figure 4b shows the overlap between the “members” of a specialist com-
munity in a period, and those in the “same” community in the previous period. It shows that
the longer a specialist community exists, the more stable its members are. This shows that epis-
temic capture is not only a matter of increasingly crystallized linkages on the level of topics,
but also reflects in the crystallization of members around epistemic structures.
Micro-level dynamics, such as the persistence or breakdown of specialist topic paths, or the
stabilization of specialists around particular communities points to the complexities of spe-
cialization at the micro-level. Epistemic capture is visible in the recurrence and persistence
of communities in time and the sudden proliferation of specialist paths at specific points in
twentieth-century debates. It also manifests as the stabilization of individual politicians as spe-
cialists in specific areas. As such our network-based approach also shows the contingencies of
epistemic capture.
5. Conclusion
Our analysis of the Dutch Lower House proceedings from 1945 to 1995 reveals the complex
dynamics between epistemic capture and specialization in parliamentary debate. By combining
analytical techniques, we demonstrate how specialization and epistemic capture unfold at the
macro, meso, and micro levels. These levels map surprisingly well onto institutional, political,
and epistemic factors, respectively, revealing the multifaceted nature of these processes. At
the macro-level, we find a declining network density, a growing modularity, and a similarly
increasing clustering stability. These trends are particularly visible in the 1950s and 1960s,
which suggests topic specialization to develop in tandem with institutional specialization the
form of permanent committees. However, given our focus on plenary debates, we show that
419
(a) Entropy of members (on x-axis) and topics (on (b) Overlap in connected members between index
y-axis) in a community path. The size of the 𝐼 and 𝐼 − 1 in a community path. Overlap is
points corresponds to the length of a path. measured with the overlap coefÏcient.
Figure 4: Membership characteristics of specialist chains in the Dutch Lower House.
this is not just a matter of procedural reform: specialisms become visible as epistemic structures
in parliamentary language.
At the meso-level, we find an interplay between political change and epistemic shifts, as
cabinet changes appear to act as moments of reconfiguration for specialist communities. The
sharp declines in neighborhood stability following cabinet changes, followed by gradual recov-
ery during a cabinet’s tenure, suggest that political transitions can disrupt the continuity of
specialist communities. Yet, the fact that these patterns are visible in the topic linkage net-
works, which are not explicitly informed by procedural or institutional factors, underscores
the epistemic dimensions of political change.
At the micro level, we identify distinct specialist chains that shed light on the dynamics of
epistemic capture within the Dutch Lower House. The increasing share of communities belong-
ing to these chains over time points to both an intensification of existing specializations and an
extension of specialization to new topics. The close correlation between the predictability of
topics and politicians within specialist chains validates the link between epistemic structures
and individual actors, while the stabilization of politician membership in longer-lasting special-
ist communities suggests a reinforcing cycle of expertise and influence. This finding highlights
how epistemic capture can become self-perpetuating, potentially limiting the diversity of per-
spectives in policy debates over time.
Our multi-level analysis offers contributions to three fields: political science, intellectual
history, and computational humanities. First, we contribute to the understanding of how spe-
cialization and epistemic capture shape the dynamics of knowledge production and decision-
making in democratic institutions. The multi-level approach highlights the complex interplay
between institutional, political, and epistemic factors in driving these processes. We, thereby,
make the (often theorized) tensions between pluralism, diversity, expertise, and efÏciency
explicit and show how twentieth-century political debate is faced with a trade-off between
breadth and depth. Further applications of our method could reveal the extent to which epis-
420
temic capture has persisted in democratic institutions, manifested in other contexts, and faced
a populist backlash in recent decades.
Second, these findings have consequences for the way historians approach the computa-
tional study of ideas and worldviews. Our analysis shows that the production, circulation, and
contestation of ideas itself changes as a consequence of specialization. It matters if ideas are
articulated in ideologically diverse and open debates, or in technical and specialist settings. To
understand these environments, computational analysis can be used beyond the level of lexical
exploration. We show that using a combination of established methodologies (topic modeling
and network analysis) can yield fresh insights in the uncharted territories of intellectual his-
tory.
Third, we have used network analysis to shed light on complex processes such as specializa-
tion. As such, this paper intends to show that iterating between distant and close reading is not
the only digital approach. Networks can elucidate regularities and contingencies at different
levels. They do not prefigure a choice between “close” and “distant”, but offer a versatile means
of differentiating between factors and contexts. However, network analysis is by no means the
only or best way to understand complex dynamics and layers of context and causality. In the
future, we aim to explore the use of agent-based modeling (ABM) could provide deeper insights
into the processes of epistemic capture and specialization. By incorporating agents represent-
ing politicians, policies, and institutional rules, ABM can offer a nuanced understanding of
how individual actions and interactions lead to macro-level phenomena, thus enhancing our
comprehension of the dynamics observed in this study.
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A. Data and Code
The full code and data for replicating our analysis will be made available from the following
Github repository upon the publication of the paper: anonymized url
B. Topic Models
The table in this appendix contains the labels and top terms for a rhetorical, procedural, and
“thematic” topic.
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Label Top Terms
Rhet/Appreciation appreciation, great statesman, good, wise, word, work,
faction, on which to speak, hope, year, special, heart,
agreement, start, new, gladly, trust, thank
Proc/Meeting report, provisional, answer, memorandum, member, remark,
Reports bill, government, reason, opinion, consider, gladly, minister,
general, explanation, different, point, relation
European European, country, politics, cooperation, international,
Community economic, community, integration, common, national,
territory, relation, foreign, large, unit, union, development,
treaty, government
C. Linkage function
Given a matrix 𝜃 ∈ ℝ𝑁 ×𝐾 , where 𝑁 is the number of documents and 𝐾 is the number of topics,
the function computes mutual information between topics.
• 𝜃𝑖𝑗 : Document-topic mixture value for document 𝑖 and topic 𝑗.
Steps:
1. Joint Probability Calculation:
∑𝑑 𝜃𝑑𝑖 ⋅ 𝜃𝑑𝑗
𝑃(𝑖, 𝑗) =
∑𝑖,𝑗 ∑𝑑 𝜃𝑑𝑖 ⋅ 𝜃𝑑𝑗
2. Marginal Probability Calculation:
∑𝑑 𝜃𝑑𝑖
𝑃(𝑖) =
∑𝑖 ∑𝑑 𝜃𝑑𝑖
3. Mutual Information:
𝑃(𝑖, 𝑗)
𝑅𝑖𝑗 = log2 ( )
𝑃(𝑖) ⋅ 𝑃(𝑗)
4. Smoothing Function:
𝑃(𝑖, 𝑗) min(𝑃(𝑖), 𝑃(𝑗))
weight(𝑖, 𝑗) = ( )( )
𝑃(𝑖, 𝑗) + 1 min(𝑃(𝑖), 𝑃(𝑗)) + 1
5. Smoothed Mutual Information:
SMI𝑖𝑗 = weight(𝑖, 𝑗) × 𝑅𝑖𝑗
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D. Community Paths
Community paths are consecutive links (paths) between similar communities in topic linkage
networks. Lines indicate connections between communities in different periods. Paths related
to foreign policy (blue), education (red), and agriculture (yellow) are highlighted.
425
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