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Record W4400482794 · doi:10.55016/ojs/cpai.v5i1.75025

Academic integrity in Canada: An author panel for this important new book

2022· article· en· W4400482794 on OpenAlexaffabout
Josh Seeland

Bibliographic record

VenueCanadian Perspectives on Academic Integrity · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicWorkplace Violence and Bullying
Canadian institutionsAssiniboine Community College
Fundersnot available
KeywordsAcademic integrityResearch integrityPolitical scienceEngineering ethicsEngineering

Abstract

fetched live from OpenAlex

“Academic integrity in Canada: an enduring and essential challenge” is an open-access book recently published by Springer as part of the series “Ethics and Integrity in Educational Contexts”. Edited by scholars Sarah Elaine Eaton and Julia Christensen Hughes, it contains over 600 pages in 31 chapters designed to address the gap in Canada’s study and evidence-based recommendations involving academic integrity. The book is divided into five sections: Canadian context, emerging and prevalent forms of academic misconduct, integrity within specific learning environments and professional programs, barriers and catalysts to academic integrity: multiple perspectives and supports, and institutional responses. In this moderated panel session, several chapter authors as well as Sarah Elaine Eaton will delve into specific aspects of their contributions towards the book. Through the open question and discussion segment, attendees will be inspired to contribute in their own institutional roles towards provincial and, ultimately, the growing Canadian academic integrity community. Learning Outcomes Discuss academic integrity culture and initiatives in Canada Identify ways to make contributions towards academic integrity in various roles and institutions Reflect on specific aspects of academic integrity covered in individual chapters

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)0.003
metaresearch head score (Gemma)0.002
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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.354
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.010
Insufficient payload (model declined to judge)0.0030.000

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 head0.073
GPT teacher head0.342
Teacher spread0.269 · 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 teacher head, not a consensus.

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
Published2022
Admission routes2
Has abstractyes

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