MétaCan
Menu
Back to cohort
Record W4214827142 · doi:10.1007/978-3-030-83255-1_25

Changing “Hearts” and Minds: Pedagogical and Institutional Practices to Foster Academic Integrity

2022· book-chapter· en· W4214827142 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEthics and integrity in educational contexts · 2022
Typebook-chapter
Languageen
FieldSocial Sciences
TopicAcademic integrity and plagiarism
Canadian institutionsUniversity of British Columbia
FundersUniversity of Guelph
KeywordsAcademic integrityMisconductConversationInstitutionPsychologyBest practicePedagogyMedical educationMathematics educationSociologyPolitical scienceSocial psychologyMedicine

Abstract

fetched live from OpenAlex

Abstract This chapter shares findings of and recommendations from a three-year initiative at the University of British Columbia to develop and assess enhanced and explicit instruction in academic integrity in first-year writing courses, an enterprise that now involves 42 faculty members teaching up to 5000 students each year. This project began from the appreciation that, as an institution, we needed to close the gap between our expectations of academic integrity and students’ understanding of those expectations, and to make explicit what is often treated as assumed understanding. This approach was intended to help students develop more robust knowledge and appreciation for academic integrity as a core element of the academic community to which they now belong. Drawing on the qualitative and quantitative data we gathered from students and faculty, including surveys, focus groups, misconduct reports, and interviews, I illustrate how what I call “pedagogies of integrity” have led to improved uptake by students (and instructors) of academic integrity as both theory and practice, resulting in a change in the number as well as type of academic misconduct cases, and have led to significant insights about the place of academic integrity in larger conversations about student belonging, wellness, and access. I share not only how the instructors in this project changed the conversation in their own courses, but also how these discussions are resonating across disciplines and faculties of our campus and beyond. Finally, I outline recommendations for next steps in policy and practice that these findings suggest.

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.

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)0.008
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.808
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0030.027
Insufficient payload (model declined to judge)0.0020.000

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 head0.257
GPT teacher head0.460
Teacher spread0.203 · 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