MétaCan
Menu
Back to cohort
Record W3021065378 · doi:10.1007/s12108-020-09449-x

Pillarization (‘Verzuiling’). On Organized ‘Self-Contained Worlds’ in the Modern World

2020· article· en· W3021065378 on OpenAlexaboutno aff
Staf Hellemans

Bibliographic record

VenueThe American Sociologist · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Capital and Networks
Canadian institutionsnot available
FundersUniversiteit van TilburgUniversiteit Gent
KeywordsSociologyPoliticsPopulationProtestantismPolitical economySocial sciencePolitical scienceLaw

Abstract

fetched live from OpenAlex

Abstract Movements and groups abound in modern society. Sometimes, a movement or group succeeds in mobilizing a large section of the population and thoroughly knitting it together, by building a pervasive subculture and by setting up a vast interrelated network of organizations, resulting in a seemingly impenetrable and powerful bloc. This happened to different degrees in most Western countries, including the United States and Canada. It is also occurring now in the non-Western world. Belgium and the Netherlands were particularly affected by extensive bloc building. In both countries, Catholic, Socialist, and Liberal pillars – plus a Protestant pillar in the Netherlands – divided society and determined political and social life from the late nineteenth century up to the late twentieth century. As a consequence, the phenomenon has been studied there more thoroughly and under a specific label, ‘verzuiling’ (pillarization). The first section of this article offers a review of pillarization theory in the Netherlands, Belgium and elsewhere. In the second part, to advance the study of organized blocs all over the world, I argue for a broad, international perspective on pillarization against the particularistic tendencies of many pillarization researchers, especially in the Netherlands. In a shorter third part, I address the isolation of pillarization theory from general sociological theory. Self-reinforcing processes of segregation and organization in large population groupings were and still are a common feature in the modern world. They have resulted in more than one case in divided societies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0040.039
Scholarly communication0.0050.006
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.039
GPT teacher head0.312
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations41
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueThe American SociologistSame topicSocial Capital and NetworksFrench-language works237,207