Community Service-Learning: Why Can’t Canada Be More Like Mexico?
Bibliographic record
Abstract
In Mexico, the community service strategy and requirements for undergraduate students are both longstanding and mandated by the Mexican Constitution. Students undertake a minimum of 480 hours of service during their undergraduate degrees, which are coordinated through their universities’ Social Service (SS) departments. Many Canadian universities and colleges offer community service through courses and volunteer programs; however, the practice and adoption levels vary widely. Student involvement with community partners, as represented through community service-learning (CSL) and volunteerism in Canada, are sponsored by many post-secondary institutions but are not driven by a national agenda. While, in Mexico, community service is documented at a departmental and institutional level for reporting to stakeholders and the government, in Canada, documentation of community service varies with the institutional mandate and is often sporadic or non-existent; the imperative for systematic student engagement and citizenship development has not been recognized at the national level. This research paper provides an overview of the community engagement practices in both countries, with the national patterns represented through a summative review of selected Canadian and Mexican universities. Suggestions for processes and practices for Canada are proposed based upon the Mexican model.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".