Co-operatives, Credit Unions and Social Engagement in Canada Undergraduate Student Paper Submitted to the AAEA paper competition
Bibliographic record
Abstract
Co-operatives serve as engines for local economies; generating and retaining local wealth, operating through existing social networks within communities and providing economic opportunities for local people. Is there a correlation between levels of social engagement and the presence of co-operatives and credit unions? Using the Statistics Canada GSS survey, 2003 and Environics Analytics, Business Locations data it is possible to assess the linkages between social engagement and the presence of co-operatives in Canada. Linkages between demographics (including the breakdown of rural/urban co-operatives and credit unions) and social engagement we isolated volunteerism as a dependant variable in logit regressions. It was established that: across Canada, the older people are, if an individual is female, the larger her or his household is, the less TV he or she watches, the more she or he uses internet, the higher the rate of highschool graduation, the more trusting people are of their neighbours, the more rural an area is, the more fully employed people are, the less likely they are to say no to volunteerism. Although, in all provinces the rural areas have higher levels of social engagement, social engagement is not a direct indicator for the existence of higher levels of co-operative businesses. 2
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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 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.001 |
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".