Beyond Employability: Defamiliarizing Work-Integrated Learning with Community-Engaged Learning
Bibliographic record
Abstract
Within the context of an increasing interest in forms of work-integrated learning (WIL) among governments and institutions of higher education, this essay explores the relation between WIL and community-engaged learning (CEL) in order to argue that the structural and self-critique apparent in much CEL scholarship can serve as a model to WIL scholars and practitioners. CEL has undergone a rigorous process of self-examination in recent years, a process that has encouraged its advocates to think carefully about their core assumptions, appropriate learning objectives, and best practices in the field. In this way, we argue, whether or not CEL is classified as a form of WIL, it can serve to defamiliarize many of WIL’s assumptions and to invite self-reflection in the field as a whole. In the first half of the essay, we provide background for the conversation, first in the Canadian context, and then in the broader scholarship of CEL. In the second half, we offer three case studies that illustrate both the distinctive characteristics of CEL and, in the last case, how these characteristics might strengthen the practice of traditional WIL.
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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 | 0.025 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.126 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".