Inciting Change Makers in an Online Community Engaged Learning Environment During Pandemic Restrictions: Lessons from a Disability Studies and Community Rehabilitation Program
Bibliographic record
Abstract
This practice-based article presents strategies employed in the shifting of the Community Engaged Learning (CEL) components of an undergraduate program in community rehabilitation and disability studies (CRDS) to an online modality during the 2020-2021 Covid-19 restrictions. The CRDS program, based in Calgary, Canada places high importance on CEL with a focus on critical engagement, mentorship, and community action for social justice. The Inciting Change Makers (ICM) framework, which we present here, is foundational to our teaching and learning in this field. During the pandemic restrictions, we found the framework not only supported us to engage learners in our focus areas for inciting change, but also provided the opportunity to consider ways that the online learning environment enhanced the CEL practica experience. Using vignettes, we demonstrate the successful use of the ICM framework in an online CEL context to develop a more authentic, engaged and inclusive community of learners. Three vignettes illustrate specific approaches used to carry out meaningful, impactful CEL opportunities in a mandated online environment. Lessons from these strategies may assist similar programs in adapting their own Community Engaged Learning programs in an increasingly online world.
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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".