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Record W4400482776 · doi:10.55016/ojs/cpai.v6i1.76531

Building a Culture of Academic Integrity through Restorative Justice

2023· article· en· W4400482776 on OpenAlexaff
Alana Abramson, Anna Beth Rucker

Bibliographic record

VenueCanadian Perspectives on Academic Integrity · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicAcademic Freedom and Politics
Canadian institutionsKwantlen Polytechnic University
Fundersnot available
KeywordsRestorative justiceAcademic integritySociologyCriminologyPolitical scienceEngineering ethicsEngineering

Abstract

fetched live from OpenAlex

Restorative justice (RJ) is a philosophy and set of values and principles that can inform justice practices and responses to harm. Its processes aim to address needs, repair or transform relationships, and promote understanding, meaningful dialogue, and direct accountability. Ethical and mindful approaches to RJ processes have the potential to address power imbalances, enhance community participation, establish relationship-based environments, and respond more meaningfully to human needs, which promotes social justice. A restorative lens has been applied to post-secondary environments in relation to academic and non-academic harms that occur on and off campus between students, faculty, and staff. This workshop will describe what we are learning through taking a restorative approach to building a culture of academic integrtity at Kwantlen Polytechnic University. Workshop participants can expect us to cover the following topics:* Describe the principles and procedures of restorative justice in relation to promoting a culture of academic integrity* Identify the benefits of restorative justice as an effective response to academic integrity violations compared to punitive approaches

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)0.002
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.572
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0020.013
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 head0.072
GPT teacher head0.395
Teacher spread0.323 · 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 designTheoretical or conceptual
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
Published2023
Admission routes1
Has abstractyes

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