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Record W7043976898

University students’ reactions to a peer’s cheating behavior

2018· dissertation· en· W7043976898 on OpenAlexafffund

Bibliographic record

VenueeScholarship@McGill (McGill) · 2018
Typedissertation
Languageen
FieldSocial Sciences
TopicAcademic integrity and plagiarism
Canadian institutionsMcGill University
FundersSocial Sciences and Humanities Research Council of CanadaMcGill University
KeywordsCheatingWitnessAcademic dishonestyGrading (engineering)Dishonesty
DOInot available

Abstract

fetched live from OpenAlex

While academic dishonesty has been a topic of inquiry since the late 1920's, research in this area has largely focused on the perpetrators of cheating and understanding why they cheat.Relatively little consideration has been given to understanding the reactions of those who witness cheating and identifying the factors that influence student observers' decisions to either report or withhold information about this kind of transgression.The current study investigated university students' reactions to an observed act of cheating in an experimental paradigm.Participants witnessed a confederate cheat during an exam and were subsequently questioned.The results suggest that while the vast majority of students did not voluntarily report the incident, most do so once they are asked relatively direct questions about the event.The findings also reveal that different grading scenarios significantly influences students' reactions to a peer's cheating behavior; yet, additional analyses indicate that this effect applies to female students only.The current study has important implications for school professionals in that it provides greater insight into university students' actual reactions to an observed act of cheating and the factors which make their decision to report the incident more or less likely.

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

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)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.962
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0060.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0010.001

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 head0.029
GPT teacher head0.317
Teacher spread0.287 · 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

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations0
Published2018
Admission routes2
Has abstractyes

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