MétaCan
Menu
Back to cohort
Record W1606412596 · doi:10.22329/celt.v4i0.3277

13. Linking Academic Integrity and Classroom Civility: Student Attitudes and Institutional Response

2011· article· en· W1606412596 on OpenAlexaffvenue
Troy Brooks, Zopito A. Marini, Jon Radue

Bibliographic record

VenueCollected Essays on Learning and Teaching · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicAcademic integrity and plagiarism
Canadian institutionsBrock University
Fundersnot available
KeywordsCivilityAcademic dishonestyIncivilityAcademic integrityCheatingPsychologyIntervention (counseling)InstitutionSocial psychologyPedagogySociologyPolitical scienceLawSocial science

Abstract

fetched live from OpenAlex

This paper explores the notion that student behaviour regarding academic integrity and classroom civility are linked, and that intervention methods used to resolve classroom incivility may be used as a response to academic dishonesty. We advance the view that academic integrity and classroom civility refer to a student’s willingness to respect the rules and regulations of the institution; and that, acts of academic dishonesty and incivility refer to student behaviour in breach of institutional policy and/or not consistent with the social norms of the institutional culture (e.g., inappropriate human interactions). The perceptions and attitudes of first-year students toward academic integrity as they transition from high school to university are examined. Two hundred and thirty-nine first-year students volunteered to participate in this study. The preliminary findings of the open ended response regarding their observations and experiences with cheating and plagiarism in high school and in university are reported with a view to offer suggestions regarding institutional intervention strategies.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.999
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.036
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.042
GPT teacher head0.336
Teacher spread0.294 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
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

Citations5
Published2011
Admission routes2
Has abstractyes

Explore more

Same venueCollected Essays on Learning and TeachingSame topicAcademic integrity and plagiarismFrench-language works237,207