Community-Engaged Learning: the National Response Within Institutions
Bibliographic record
Abstract
Community-engaged learning (CEL) has become a relevant part of the current experiential and community culture, with more than 30 universities in Canada (and likely more) partaking in some form of CEL at their institution. However, how does each institution implement CEL, and how accessible are digital media and resources for students and communities who want to learn more about CEL at these institutions? Our research primarily focuses on data collection from digital media (e.g., websites, articles, Google searches) and draws conclusions based on our findings. Our team focuses on rating the overall culture of CEL in Canada based on the accessibility, significance, and apparent integration of CEL throughout each institution. Our environmental scan of these factors will determine if CEL is truly becoming a new topic of community culture and if it is worth exercising resources to develop and interrogate CEL at institutions across Canada further.\nThis presentation was created for the purpose of the USRI conference. However, this project will be further continued in a follow-up academic article that will break down the data and significance of CEL at Canadian public institutions with assistance from the team of Dr. Sandra Smeltzer, Dr. Basil Chiasson, Amala Poli, and Giada Ferrucci.
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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.011 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.002 | 0.024 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".