Pillarization (‘Verzuiling’). On Organized ‘Self-Contained Worlds’ in the Modern World
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.039 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".