Bibliographic record
Abstract
How does decolonization inform co-operative education (co-op)? This question raises complex issues for educators and institutions, especially considering how decolonization is an unsettling journey (Regan, 2010) that involves critical reflexive change. Facing increasing pressures to support 21st century skills and career development—pressures that often mirror neoliberal socio-economic priorities of efficiency, growth, instrumentality, and productivity—it can be hard to know where to begin engaging decolonization in co-op. This article explores theoretical discussions for how “decolonial praxis” (Gahman & Legault, 2019) can inform an approach to co-op that equips students to engage their integrative career development in holistic and responsible ways. Drawing from the work of curriculum theorist Dwayne Donald (2022), I will suggest that an important starting point involves practices of unlearning and relationality within co-op curriculum and programming. Practices of unlearning involve examining assumptions in co-op and assessing areas for change (e.g. values of neoliberal capitalism). Practices of relationality emphasize ways co-op can support student growth and responsibility within their own workplaces and communities. I conclude with a brief case study discussing how these directions have informed decolonial directions in unsettling co-op at the University of the Fraser Valley (Abbotsford, British Columbia).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".