Does geographic context influence employability‐motivated volunteering? The role of area‐level material insecurity and urbanicity
Bibliographic record
Abstract
There is a growing public discourse that volunteering increases the likelihood of finding a better job because it improves social and human capital. While previous studies have largely treated volunteers' motivations as individualistically determined, contextual determinants of volunteers' motivations are relatively neglected. The purpose of this study is to understand the individual and contextual characteristics in which individuals are more likely to volunteer as a means of improving their employability. Using a random sample of 768 volunteers across Canada, we estimate the independent effects of a) municipal‐level material insecurity, b) urbanicity, and c) individual characteristics on the odds of “volunteering to improve employability.” Our findings show that living in municipalities with high economic insecurity and in urban settings independently increases the odds of volunteering to improve employability. Our study points to evidence of model misspecification, by omission of unobserved geographical covariates, in previous studies of volunteers' motivations.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".