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Record W4390772842 · doi:10.59942/2995-9063.1002

More Obstacles for the Graduate Student Author: Open Access ETDs Trigger Plagiarism Detectors

2023· article· en· W4390772842 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Graduate Librarianship · 2023
Typearticle
Languageen
FieldDecision Sciences
TopicScientific Computing and Data Management
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsSuspectInclusion (mineral)Plagiarism detectionPublishingInstitutionLibrary scienceGraduate studentsOpen source softwareWorld Wide WebComputer scienceSoftwarePolitical scienceMedical educationSociologyMedicineLaw

Abstract

fetched live from OpenAlex

Supporting graduate students as authors is one of the many services we provide at the University Library, University of Saskatchewan (USask). Graduate students often submit articles to journals based on content from their electronic theses or dissertations (ETDs). Recently, we have noticed an increase in the number of such article submissions being flagged for possible rejection on “plagiarism” or “prior publication” grounds. We suspect this may be because plagiarism detection software is increasingly being integrated into publishers’ article submission systems. This software is triggered by the existence of the student’s open access (OA) ETD in our institutional repository. This happens despite OA ETD inclusion in repositories being a common practice and despite journal policies often allowing submission of articles based on ETDs. We review common practices and guidelines around publishing of ETD content, two recent cases of journals initially rejecting such submissions by graduate student authors of our institution, and our reflections on this issue and how to address it.

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
gemmaOpen scienceResearch integrity
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativemedium
gptResearch integrityScholarly communication
Domain: not available · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptualmedium
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)0.023
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.636
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0230.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.004
Science and technology studies0.0010.000
Scholarly communication0.0140.004
Open science0.0130.004
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 head0.762
GPT teacher head0.559
Teacher spread0.202 · 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