Bibliographic record
Abstract
Residents of Québec typically give less money and volunteer less time compared to residents of all other provinces. This article employs the most recent General Social Survey: Giving, Volunteering and Participating (2013) data set and Tobit procedures and finds that Quebeckers give less money largely because of smaller endowments of two important determinants, religiosity and household income. Once demographic and socioeconomic characteristics are controlled, Quebeckers’ financial donations are comparable to those of residents of Ontario and Atlantic Canada and exceed those of residents of British Columbia. Quebeckers moreover are similar to others when it comes to volunteering for religious organizations, but they volunteer significantly less than others for secular organizations, which cannot be explained in this article.Typiquement, les résidents du Québec donnent moins d’argent et consacrent moins de temps au bénévolat que les résidents des autres provinces. Cet article, en recourant aux données provenant de la dernière « Enquête sociale générale : dons, bénévolat et participation, 2013 » et au modèle Tobit, conclut que les Québécois donnent moins d’argent en grande partie parce qu’ils ont des lacunes dans deux domaines importants, à savoir la religiosité et le revenu du ménage. Cependant, après un contrôle des caractéristiques démographiques et socioéconomiques, on constate que les dons de la part des Québécois sont au fait comparables à ceux des résidents de l’Ontario et des provinces de l’Atlantique et supérieures à ceux des résidents de la Colombie-Britannique. D’autre part, les Québécois sont comparables aux résidents des autres provinces pour ce qui est du bénévolat dans les organismes religieux, mais ils font beaucoup moins de bénévolat dans les organismes séculaires, fait que cet article ne parvient pas à expliquer.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 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) | 0.006 | 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".