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Record W4400482808 · doi:10.55016/ojs/cpai.v6i2.77102

An Attribution Theory Lens on Plagiarism: Examining the Beliefs of Preservice Teachers

2023· article· en· W4400482808 on OpenAlexaff
Lauren D. Goegan, Lia M. Daniels

Bibliographic record

VenueCanadian Perspectives on Academic Integrity · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicAcademic integrity and plagiarism
Canadian institutionsUniversity of AlbertaUniversity of Manitoba
Fundersnot available
KeywordsAttributionPsychologyThrough-the-lens meteringLens (geology)Mathematics educationSocial psychologyOpticsPhysics

Abstract

fetched live from OpenAlex

Academic misconduct is a prominent issue at postsecondary institutions. This issue includes the act of plagiarism, which has received considerable attention on campuses. There is a growing body of research examining why students engage in plagiarism, and what they know about plagiarism, but little of this research is guided by a theoretical framework. Although all students may be tempted to plagiarize, students in teacher education programs represent a unique population because they are concerned with developing their own academic performance alongside the skills necessary to manage situations of academic misconduct as future teachers. Therefore, our first aim was to examine preservice teachers’ beliefs about plagiarism. Then, following the principles of Attribution Theory, our second aim was to investigate how beliefs of controllability related to acts of plagiarism impacted participants views on responsibility, emotions, help giving, and reporting. We used a within-person repeated measures design with three levels of controllability manipulated through hypothetical scenarios of plagiarism to collect data from 201 preservice teachers. Overall, preservice teachers had strong beliefs about plagiarism. Moreover, when scenarios included students who engaged in plagiarism that was controllable, participants were more likely to view the student as responsible, feel anger towards them, support punishment, and recommend reporting the student, than when the act of plagiarism was not seen as controllable. We provide recommendations for instructors and administrators for supporting students and highlight limitations and directions for future research.

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
gemmaResearch integrity
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativemedium
gptResearch integrity
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
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)0.010
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.718
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0020.012
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 head0.064
GPT teacher head0.344
Teacher spread0.280 · 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 designQualitative · Observational
Domainnot available
GenreEmpirical

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

Citations1
Published2023
Admission routes1
Has abstractyes

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