Editorial: York Symposium on the Scholarship of Engagement
Bibliographic record
Abstract
Over the past several years, community engagement has emerged as a priority for universities.Many institutions now try to frame themselves as "an engaged university, " suggesting a different way of doing business.But what do such universities mean by this?Community engagement can be framed under teaching, research, or service priorities (or any combination thereof), and such engagement can be supported by local, national, and/or international networks.Examples of Canadian support for local community university initiatives include the Institute for Community Engaged Scholarship (University of Guelph), Services aux Collectivités (Université du Québec à Montréal), and the Harris Centre (Memorial University of Newfoundland and Labrador).ere are national networks supporting engagement, such as Community Based Research Canada, Canadian Alliance for Community Service Learning, Community Campus Collaborations Initiative, and ResearchImpact-RéseauImpactRecherche.ere are also international networks seeking to build capacity for community engagement, such as the Living Knowledge Network, Global Alliance for Community Engaged Research, and the Global University Network for Innovation.York University actively supports researchers and students participating in engaged scholarship.Faculty(ies) working in a truly interdisciplinary manner support engagement because communities do not live in disciplinary silos.Interdisciplinarity and active support of engagement are two elements that set the scholarship of the York University apart.
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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 | 0.014 | 0.055 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.009 | 0.003 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.027 | 0.032 |
| Insufficient payload (model declined to judge) | 0.023 | 0.011 |
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".