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Record W4410559479 · doi:10.34314/9a833d76

Unsettling Cooperative Education: Decolonial Directions

2025· article· en· W4410559479 on OpenAlexaff
David Warkentin

Bibliographic record

VenueJournal for the Study of Cooperative and Experiential Education · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Educational Policies and Reforms
Canadian institutionsUniversity of the Fraser Valley
Fundersnot available
KeywordsSociologyEpistemologyMathematics educationPsychologyPhilosophy

Abstract

fetched live from OpenAlex

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).

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.318
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.402
Teacher spread0.387 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

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