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Record W4412442268 · doi:10.7202/1118737ar

Academic Integrity in Selected Western Canadian Colleges and Polytechnics: A Policy Analysis

2025· article· en· W4412442268 on OpenAlexaffvenueabout
Lisa Vogt, Sarah Elaine Eaton, Brenda M. Stoesz, Josh Seeland

Bibliographic record

VenueCanadian Journal of Educational Administration and Policy · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicAcademic integrity and plagiarism
Canadian institutionsUniversity of ManitobaUniversity of CalgaryAssiniboine Community CollegeRed River College
Fundersnot available
KeywordsHigher educationPolitical scienceSociologyPedagogyMathematics educationPsychologyEconomic growthEconomics

Abstract

fetched live from OpenAlex

Canadian colleges and polytechnics have been neglected in research on academic integrity, with some exceptions. Therefore, we examined academic integrity policy documents (N = 36) from 16 publicly-funded colleges and polytechnics in Alberta and Manitoba, Canada, replicating a qualitative research design used in previous research. Data were analyzed through the lens of five core elements of exemplary academic integrity policy. Access to policy was straightforward using the search engine present on institutional websites. In terms of approach, the three most frequently identified principles were natural justice/procedural fairness/timeliness, punitive, and ethics/integrity values/standards. The student was identified as the locus of responsibility for upholding academic integrity as a matter of student conduct, with faculty and administrators responsible for investigating and addressing misconduct after cases come to light. Within the documents, detail was extensive with plagiarism, cheating, breaches of exam integrity, collusion, falsification, fabrication, and intentional misrepresentation describing misconduct most commonly. Most documents described supports in the form of procedural steps for reporting academic misconduct, with minimal mention of proactive or remedial education. We also examined whether equity, diversity, inclusion, accessibility, decolonization, and Indigenization (EDIA-DI) were considered within these documents and found little attention was paid to these values. We call for colleges and polytechnics to update approaches to policy design that include a focus on EDIA-DI, connect academic integrity and professional ethics to educate students on institutional expectations and conduct more research to inform the development of strategies that nurture cultures of academic integrity.

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)0.018
metaresearch head score (Gemma)0.046
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.997
Threshold uncertainty score0.719

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.046
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0120.031
Science and technology studies0.0340.007
Scholarly communication0.0120.002
Open science0.0050.005
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.019
GPT teacher head0.364
Teacher spread0.345 · 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 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

Citations2
Published2025
Admission routes3
Has abstractyes

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