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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 OpenAlexaff
Mary‐Lee Mulholland

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.

How this classification was reachedexpand

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 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)0.021
metaresearch head score (Gemma)0.089
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 score0.996
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.089
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0110.043
Scholarly communication0.0150.010
Open science0.0020.011
Research integrity0.0040.008
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 head0.023
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

Classification

machine, unvalidated

Labeled directly by 2 models reading the full record.

Research integrity

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical · Other

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

Citations10
Published2020
Admission routes1
Has abstractyes

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