Re-Defining Academic Integrity: Embracing Indigenous Truths
Bibliographic record
Abstract
Abstract Despite historical and ongoing challenges, Canada has been making promising strides towards reconciliation prompted in large part by the work of the Truth and Reconciliation Commission of Canada (2015). We honour our Indigenous Elders and Ancestors who have led social and educational movements that named and resisted the negative outcomes created and continued by a Canadian colonial history. The authors point to current institutional projects of decolonizing and Indigenizing the academy as holding the potential to re-define what academic integrity means. As a hopeful point of entry into how teaching and learning scholars might reconsider current conceptions of integrity, we see Indigenizing efforts across a number of Canadian universities as the basis from which to speak to a more inclusive and wholistic definition of academic integrity. The authors seek to problematize the current neoliberal and commercialized approaches to education where different forms of academic misconduct arise as inevitable outcomes. If education is viewed as the pursuit of truth, or more appropriately truths, then it is essential to nuance the scope of academic integrity to include Indigenous perspectives such aswholismandinterconnectedness. In this chapter, we discuss these truths, challenging current conceptions, to propose a more inclusive definition of academic integrity by drawing upon Indigenous scholarship as well as dynamic forms of ancestral language to situate our work. In sum, sharing truths through the inclusion of Indigenous perspectives grounds the scholarly discussion in an equitable understanding of truth-telling as foundational to academic integrity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.038 | 0.127 |
| Scholarly communication | 0.032 | 0.013 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".