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The construction of plagiarism in a school of nursing

2003· article· en· W2027235867 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

VenueLearning in Health and Social Care · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicAcademic integrity and plagiarism
Canadian institutionsUniversity of ManitobaUniversity of British ColumbiaUniversity of British Columbia Hospital
Fundersnot available
KeywordsSyllabusAcademic integrityIgnoranceMedical educationInclusion (mineral)PsychologyPedagogyMedicinePolitical scienceSocial psychologyLaw

Abstract

fetched live from OpenAlex

Abstract Despite clear policies for handling reported occurrences of plagiarism, and the inclusion of antiplagiarism statements in course syllabi and university calendars, reports of both student and faculty plagiarism in universities has increased dramatically in the past decade. Critics indicate that current approaches to prevent plagiarism in universities are limited by their focus on the individual and by their failure to consider the contextual influences in university settings. Eight faculty members and 10 students in a university school of nursing were interviewed about their understanding of, and response to, plagiarism. The participants viewed plagiarism primarily as a student problem caused by moral breakdown or ignorance. Faculty indicated that they most often overlooked university policy for reporting plagiarism if the offence was deemed as unintentional or caused by personal stress. Students were aware that plagiarism was wrong, but undergraduate students frequently conceptualized it as an ‘academic quirk’. The article concludes with a discussion of the implications of the research findings to university schools of nursing.

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
Qualitativelow
gptMetaresearchResearch integrity
Domain: Methods · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativehigh
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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.502
Threshold uncertainty score0.774

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 science0.0000.000
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 head0.022
GPT teacher head0.349
Teacher spread0.327 · 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