Build it and they will come: Volunteer opportunities and volunteering
Bibliographic record
Abstract
Abstract Formal volunteering refers to an individual's unpaid contribution of time to the activities of a charitable or non‐profit organization. While the physical presence of these organizations is usually required for citizens who want to volunteer, neighbourhoods vary with respect to the amount of volunteering opportunities available. We are the first to geo‐code information on the location of registered charities and the location of individuals, using full six‐digit postal codes, to examine how the physical proximity of charities affects the decision to volunteer. We carefully address the possibility that proximity to charities might be endogenous: organizations and volunteers may respond to similar unobservable factors when deciding where to locate. Our results imply that access does matter for the decision to volunteer: one more charity within a 1 km buffer around an individual's residence increases the predicted probability of volunteering by 0.8%. The impact of an additional charity on the likelihood of volunteering decreases with distance from the individual's residence and is more pronounced for urban dwellers, providing further evidence that the location of charities matters when it comes to nudging individuals to volunteer.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".