Equity, Diversity, Inclusion, Accessibility, Decolonization and Indigenization in Academic and Research Integrity: Policy, Procedure, and Praxis
Bibliographic record
Abstract
In this session we provide international perspectives on equity, diversity, inclusion, accessibility and decolonization of academic and research integrity. Academic integrity has long been viewed as a matter of student conduct, whereas research integrity has been focused on research and academic staff. In this session, we take a more holistic, multi-stakeholder transdisciplinary view in which students, educators, leaders, and staff all have a role to play in upholding integrity. There is evidence from elsewhere (e.g., Australia, UK, US) to suggest that there is over-representation in misconduct reporting in universities, with people from Asia and Africa being among the most over-represented groups. Currently, there are no data available in Canada to our knowledge, a knowledge deficit that we problematize and explore. Evidence from elsewhere suggests that individuals from particular equity deserving groups may be hypersurveilled (i.e., persons of colour, those who speak English as an Additional Language and those with disabilities). We challenge assumptions made about international students with regards to academic cheating that are not supported by empirical research. We trouble the ways in which students are treated as a homogeneous group, both in terms of academic development, as well as in misconduct case investigation and processing. Further, we explore possible connections between the neoliberal university system in Canada that prioritizes international students (and the extra tuition they pay) with inadequate resources to support their academic success. This lack of support regarding academic and research integrity may contribute to some students being unjustly (hyper)surveilled and reported for misconduct compared to domestic students, particularly those from culturally and linguistically dominant groups. Moreover, efforts to properly embrace complementary perspectives on academic integrity are sometimes absent or recently emerging in higher education institutions, which might complicate chances to involve the educational communities’ stakeholders in ways that lead to more dialogue and understanding to adequately address the needs of increasingly diverse communities in Canadian Post Secondary Education. This is a collaborative presentation showcasing the research of doctoral students and senior researcher whose scholarship focuses on ethics and equity in higher education.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.020 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".