Why Get Involved? Reasons for Voluntary-Association Activity Among Americans and Canadians
Bibliographic record
Abstract
Using national representative sample survey data from the United States and Canada, the authors compare American and Canadian responses to a set of 14 possible reasons for being active in voluntary associations. They assess the 14 reasons individually and then conduct analyses in which the 14 measures are grouped into two composite scales: collective reasons and self-oriented motivations. The authors also consider theories for explaining how and why Americans and Canadians might differ in their motivations for volunteering. Analyses are conducted on seven background predictors: gender, race, religious affiliation, religious attendance, age, education, and socioeconomic status. Findings show that Americans are more likely than Canadians to mention altruistic rather than personal reasons for joining voluntary organizations, and Canadians are slightly more likely than Americans to emphasize personal reasons for their volunteer work, but this difference is not significant after controls. The implications of the findings for understanding voluntary activity in the two nations are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".