Nations of Joiners: Explaining Voluntary Association Membership in Democratic Societies
Bibliographic record
Abstract
Levels of voluntary association membership for 33 democratic countries are compared using data from surveys of nationally representative samples of adults from the 1990s. Four explanations of national differences in association involvement are identified and tested: economic development, religious composition, type of polity, and years of continuous democracy. The analyses consider total and working association memberships, both including and excluding unions and religious associations. Americans volunteer at rates above the average for all nations on each measure, but they are often matched and surpassed by those of several other countries, notably the Netherlands, Canada, and a number of Nordic nations, including Iceland, Sweden, and Norway. Hierarchical linear models show that voluntarism tends to be particularly high in nations that have: (1) multidenominational Christian or predominantly Protestant religious compositions, (2) prolonged and continuous experience with democratic institutions, (3) social democratic or liberal democratic political systems, and (4) high levels of economic development. With some exceptions for working memberships, these factors, both separately and in combination, are clearly important predictors of cross-national variation in voluntary association membership.
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 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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".