Voluntary Participation in Community Economic Development in Canada: An Empirical Analysis
Bibliographic record
Abstract
ABSTRACT This article is an empirical analysis of an individual's decision to participate in community economic development (CED) initiatives in Canada. The objective of the analysis is to better understand how individuals make decisions to volunteer time toward CED initiatives and to determine whether the determinants of participation in CED are unique when compared to those of participation in volunteer activities in general. The dataset employed is Statistics Canada's 2004 Canada Survey of Giving, Volunteering and Participating (CSGVP). To date, there has been no prior econometric analysis of the decision to participate in community economic development initiatives in Canada. Results suggest a role for both public policymakers and practitioners in influencing participation in CED. Résumé Cet article constitue une analyse empirique du processus de prise de décision chez les individus en ce qui a trait à la participation aux initiatives canadiennes de développement économique communautaire (DÉC). Le but de l'analyse est de mieux comprendre la façon dont les individus prennent la décision de consacrer du temps au bénévolat dans les initiatives de DÉC. Elle sert aussi à trancher la question de savoir si les facteurs de participation aux initiatives de développement économique communautaire sont uniques ou communs à la participation à des activités bénévoles en général. Les données employées dans le cadre de cette analyse sont puisées de l'Enquête canadienne sur le don, le bénévolat et la participation effectuée par Statistique Canada en 2004. À ce jour, aucune analyse économétrique n'a été menée sur la décision de participer aux initiatives canadiennes de DÉC. Les résultats suggèrent que les responsables de l'élaboration des politiques ainsi que les praticiens influencent tous deux la participation aux initiatives de DÉC.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".