Volunteering, Income Support Programs and Disabled Persons†
Bibliographic record
Abstract
We study the propensity of disabled persons to engage in volunteer activity with the Participation and Activity Limitation Survey (PALS) -- a unique Canadian dataset which provides extensive information on disabled persons as well as volunteering behaviour. Our principal focus is on the effects of various income support programs on disabled person’s participation in volunteer activities. We find that certain income support programs (e.g., workers’ compensation) are associated with decreases in the probability of volunteering while others (e.g., Pension Plans) are associated with increases in the propensity to volunteer. The reason is that not all income support programs are identical with respect to their implications for unpaid work. There are some – like workers compensation – that embody strong disincentives to volunteering while others like public Pensions that explicitly encourage unpaid work. Our conclusion is that program characteristics can significantly affect volunteering. This conclusion is further supported when we look at other income support programs that embody ambiguous or no incentive effects. As one would anticipate, these ‘incentive neutral’ programs have no significant impact on volunteering. The relevance of these results to both theories of volunteerism and public policy is discussed.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".