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Record W2189671353 · doi:10.55016/ojs/ajer.v60i2.55898

Examining the Research Base on University Co-operative Education in Light of the Neoliberal Challenge to Liberal Education

2015· article· en· W2189671353 on OpenAlexaffvenueabout
Peter Milley, Thursica Kovinthan

Bibliographic record

VenueAlberta Journal of Educational Research · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicEducational Reforms and Innovations
Canadian institutionsUniversity of Ottawa
FundersPrinceton University
KeywordsBase (topology)Liberal educationSociologyNeoliberalism (international relations)PedagogyHigher educationPolitical scienceMathematics educationPublic administrationLiberal arts educationPsychologyPolitical economyLawMathematics

Abstract

fetched live from OpenAlex

Debates have been taking place in higher education communities in Canada and other Anglo-American contexts between defenders of liberal education and promoters of neoliberalism. One development not addressed is the growth of co-operative education (co-op). The origins of co-op may reside in John Dewey’s (1939, 1966) ideas about experience and democracy, but co-op also resembles a neoliberal phenomenon. We reviewed the North American literature on co-op from 1990-2014 to see if and how the rise of co-op has posed a challenge to liberal education. Our analysis revealed a dominant focus on instrumental and economic purposes reflecting neoliberal reforms, strands of philosophical and empirical inquiry consistent with liberal education, and a notable absence of critical, emancipatory outlooks. We contend that co-op researchers need to rediscover the socially progressive promise of experiential education, informed by other educational subfields. We also argue that researchers interested in neoliberal challenges to liberal education need to tap co-op as a site of inquiry.

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 imitation

Not 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.

metaresearch head score (Codex)0.028
metaresearch head score (Gemma)0.065
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.972
Threshold uncertainty score0.150

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.065
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0110.020
Science and technology studies0.0080.041
Scholarly communication0.0180.020
Open science0.0030.010
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.149
GPT teacher head0.416
Teacher spread0.267 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
DomainMethods
GenreReview

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

Citations7
Published2015
Admission routes3
Has abstractyes

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