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Record W2990195677 · doi:10.1007/s40979-019-0047-z

Are Canadian professors teaching the skills and knowledge students need to prevent plagiarism?

2019· article· en· W2990195677 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

VenueInternational Journal for Educational Integrity · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicAcademic integrity and plagiarism
Canadian institutionsUniversité du Québec en Outaouais
Fundersnot available
KeywordsLibrary scienceRedactionPlagiarism detectionPedagogyPsychologySociologyMathematics educationComputer scienceArtLiterature

Abstract

fetched live from OpenAlex

Abstract Max 150 words. If possible, please submit your abstract in both English and French. When writing an assignment, most students start by searching for information online, which they integrate in their writing and conclude by producing a bibliography for the sources used. They use their informational, writing and referencing skills to do this as well as refer to their plagiarism knowledge to make sure their text is exempt from plagiarism. In this paper, we examined which skills and knowledge students feel the need to further develop in university to prevent plagiarism in their assignments. Professors were also questioned as to their perceptions of their students’ skills development during their pre-university studies. Questionnaires were administered in six Quebec Universities to students ( n = 1170) and professors ( n = 279). Results show that students feel the need for more training while professors expect students to have already mastered the skills and knowledge to prevent plagiarism. Recommendations are made on how to implement better training for students through a program approach. Lors de la rédaction d’un devoir, la plupart des étudiants universitaires commencent par chercher des informations en ligne, qu’ils intègrent dans leur rédaction et terminent en produisant une bibliographie des sources utilisées. Ils utilisent leurs compétences informationnelles, rédactionnelles, et de référencement documentaire et se réfèrent à leurs connaissances en matière de plagiat pour s’assurer que leur texte en soit exempt. Dans cet article, nous avons examiné les compétences et les connaissances que les étudiants ressentent le besoin de développer davantage à l’université pour prévenir le plagiat dans leurs travaux. Les professeurs ont également été interrogés sur leur perception du développement des compétences de leurs étudiants durant leurs études pré-universitaires. Des questionnaires ont été administrés dans six universités québécoises à des étudiants ( n = 1170) et à des professeurs ( n = 279). Les résultats montrent que les étudiants ressentent le besoin d’une formation plus poussée alors que les professeurs s’attendent à ce que les étudiants maîtrisent déjà les compétences et les connaissances nécessaires pour prévenir le plagiat. Des recommandations sont formulées sur la façon de mettre en œuvre une meilleure formation pour les étudiants par le biais d’une approche-programme.

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
gemmaResearch integrity
Domain: not available · Genre: Empirical
About the Canadian research system: yes · About a Canadian topic: yes
Observationallow
gptMetaresearchResearch integrity
Domain: Methods · Genre: Empirical
About the Canadian research system: yes · About a Canadian topic: yes
Observationalhigh
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)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.501
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.003
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 head0.030
GPT teacher head0.425
Teacher spread0.395 · 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