Community Service-Learning in Canada: Emerging Conversations
Bibliographic record
Abstract
This special issue invites engaged learning practitioners and scholars, both established and emerging, to take stock of the history of CSL, assess current practices, and consider how to move forward in the future. Is CSL the biggest thing to hit Canadian campuses since the late 1990s? With approximately fifty CSL programs or units across the country (Dorow et al., 2013), annual gatherings of scholars and practitioners, and a network of individuals who remain devoted to CSL despite challenges in funding and logistics, CSL in Canada has certainly made its mark, embedded in the context of a larger movement of engaged scholarship on campuses across the country—a movement exemplified in this very Engaged Scholar Journal, the first of its kind in Canada to focus on publishing community-engaged work.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.076 | 0.019 |
| Scholarly communication | 0.024 | 0.009 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.014 | 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".