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Record W1992390197 · doi:10.1177/002071520204300201

The Roots of Civil Society: A Model of Voluntary Association Prevalence Applied to Data on Larger Contemporary Nations

2002· article· en· W1992390197 on OpenAlexvenueno aff
David Horton Smith, Ce Shen

Bibliographic record

VenueInternational Journal of Comparative Sociology · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Capital and Networks
Canadian institutionsnot available
Fundersnot available
KeywordsVoluntary associationCivil societySocial capitalPoliticsDemocracyCitizen journalismPopulationDevelopment economicsPolitical economyPolitical scienceSociologyEconomicsSocial scienceLawDemography

Abstract

fetched live from OpenAlex

Based on a literature review, a theory of voluntary association prevalence in nations of the world is proposed. Greater associational prevalence is hypothesized to result from certain societal background factors (greater population size, and more favorable historical/cultural/environmental interface), aspects of basic societal structure (more permissive political control, greater modernization, more developed non-associational organizational field, and greater ethno-religious heterogeneity), and societal mobilization factors (aggregate resource mobilization for associations, aggregate social cohesion). Archival data on larger contemporary nations strongly confirm most of the model independently for two separate time periods, the 1970s and early 1990s. The ethno-religious heterogeneity variable is not confirmed as significant. No suitable data were available to test aggregate social cohesion as part of the empirical model tested. The results have important policy implications for the roots of civil society, political pluralism, and participatory democracy, partially as manifestations of social capital in a society.

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)0.039
metaresearch head score (Gemma)0.118
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.208

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0390.118
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0110.010
Science and technology studies0.0020.009
Scholarly communication0.0050.011
Open science0.0030.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.135
GPT teacher head0.380
Teacher spread0.245 · 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 designSimulation or modeling
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

Citations67
Published2002
Admission routes1
Has abstractyes

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