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Record W3033253180 · doi:10.1080/2194587x.2020.1741394

Honor and Shame: Plagiarism and Governing Student Morality

2020· article· en· W3033253180 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of College and Character · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicAcademic integrity and plagiarism
Canadian institutionsMount Royal University
Fundersnot available
KeywordsHonorHonestyMoralityShamePunitive damagesSociologyMoral developmentSocial psychologyPsychologyPedagogyLawPolitical science

Abstract

fetched live from OpenAlex

With the perceived increase in plagiarism in post-secondary institutions, there has been a simultaneous increase in research and analysis on the issue emerging from multiple fields including education, humanities, social science, business and management, sciences, and the media. The focus of this research ranges from the frequency of cases, student and faculty perception, preventative and punitive measures, and critiques of definitions and policies. In regards to the latter, many researchers have argued that plagiarism is based on antiquated notions of self, originality, and authenticity that fail to capture the important distinction between students who intend to plagiarize and those who do not. To the point, current policies on plagiarism are always embedded in a moral discourse of honor, integrity, honesty, and student codes of conduct. The problem with this approach is that student’s morality is the focus, rather than a matrix of psychological, educational, socio-economic, and cultural factors. Any attempt to respond to plagiarism as a complex and nuanced problem will require a rethinking of current policy. Using a Foucauldian framework, this article illustrates how current policy is embedded in a discourse of morality that casts students as either moral (honorable) or immoral (shameful).

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.

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptuallow
gptResearch integrity
Domain: not available · Genre: Other
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptuallow
models splitAgreement compares identical category sets and study designs across arms.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.618
Threshold uncertainty score0.422

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.023
GPT teacher head0.295
Teacher spread0.271 · 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