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Record W3209674568 · doi:10.7202/1083333ar

Academic Integrity Policy Analysis of Publicly-Funded Universities in Ontario, Canada: A Focus on Contract Cheating

2021· article· en· W3209674568 on OpenAlexaffvenueabout
Jennie Miron, Amanda McKenzie, Sarah Elaine Eaton, Brenda M. Stoesz, Emma J. Thacker, Lisa Devereaux, Nira Persaud, Marcia Steeves, Kate Rowbotham

Bibliographic record

VenueCanadian Journal of Educational Administration and Policy · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicAcademic integrity and plagiarism
Canadian institutionsFleming CollegeUniversity of CalgaryUniversity of TorontoUniversity of WaterlooUniversity of ManitobaHumber Polytechnic
FundersUniversity of South Australia
KeywordsCheatingAcademic integrityPunitive damagesPublic relationsContract managementPromotion (chess)Political scienceBusinessPsychologyMarketingSocial psychologyLaw

Abstract

fetched live from OpenAlex

In this article we report findings from a review of universities’ academic integrity policies in Ontario, Canada. The research team systematically extracted, reviewed, and evaluated information from policy documents in an effort to understand how these documents described contract cheating in Ontario universities (n = 21). In all, 23 policies were examined for contract cheating language. The elements of access, approach, responsibility, detail, and support were examined and critiqued. Additionally, document type, document title and concept(s), specific contract cheating language, presence of contract cheating definitions and policy principles were reviewed. Findings revealed that none of the universities’ policies met all of the core elements of exemplary policy, were reviewed and revised with less frequency than their college counterparts, lacked language specific to contract cheating, and were more frequently focused on punitive rather than educative approaches. These findings confirm that there is further opportunity for policy development related to the promotion of academic integrity and the prevention of contract cheating.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.776
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.334
Teacher spread0.301 · 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 designObservational
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

Citations43
Published2021
Admission routes3
Has abstractyes

Explore more

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