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Record W6922045517 · doi:10.11575/prism/41418

Academic integrity policy analysis of Alberta and Manitoba colleges

2023· other· en· W6922045517 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Calgary · 2023
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsMisconductAcademic integrityContext (archaeology)Academic communityHigher educationPolicy analysisResearch integrity

Abstract

fetched live from OpenAlex

Dealing with matters related to academic integrity and academic misconduct can be challenging in higher education. As a result, students, educators, administrators, and other higher education professionals look to policy and procedures to help guide them through these complex situations. Policies are often representative of an institution’s culture of academic integrity. For these and other reasons it is therefore important that policies and procedures are reviewed regularly and updated to ensure that they align with current educational expectations and societal context. In this presentation, we share the results from our policy analysis of 16 colleges in the Canadian western provinces of Alberta and Manitoba. Data extraction and analyses were performed using a tool developed based on Bretag et al.’s five core elements of exemplary academic integrity policy. Our results showed inconsistencies in college polices in terms of the intended audience for the documents (e.g., students, faculty, administrators), varying levels of detail, inconsistent definitions, or categories of misconduct (e.g., plagiarism, cheating) and little mention of contract cheating. We compare the results of this study with previous academic integrity policy research in Canada for colleges in Ontario (Stoesz et al., 2019), as well as universities (Miron et al., 2021; Stoesz and Eaton, 2020). We also discuss the recent increase in the use of artificial intelligence tools such as ChatGPT and GPT-3 and what this could mean in the context of academic integrity policy. We conclude with recommendations for policy reform in the Canadian college context. Our findings may be useful to those working in community colleges and polytechnics. This project is part of a larger project on academic integrity policy in Canada (https://osf.io/n9kwt/ ) Cite as: Eaton, S. E., Vogt, L., Seeland, J., & Stoesz, B. M. (2023). Academic integrity policy analysis of Alberta and Manitoba colleges. Canadian Symposium on Academic Integrity (CSAI), Univeristy of Manitoba, Winnipeg, MB.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.076
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.016
GPT teacher head0.241
Teacher spread0.225 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2023
Admission routes1
Has abstractyes

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