Academic Integrity Across Time and Place: Higher Education’s Questionable Moral Calling
Bibliographic record
Abstract
Abstract In this chapter, I call on Canada’s higher education institutions to embrace Veritas (truth), in every aspect of the academy. Academic integrity must transcend discussions of student misconduct and apply to all that we are—our history, our research, our curriculum, our pedagogy, our purpose. Tracing Western higher education’s development from medieval times in Europe, through to the US and Canada, I make the case that the academy has paradoxically been both a dominating and liberating force since its inception. While imposing Western conceptions of morality and truth that have shifted over time, and supporting the imperialist ambitions of Church, monarchy and state, higher education has also elevated its graduates to positions of influence within society and advanced national aims. Despite credos of truth telling and missions of character development, higher education’s moral calling has been—and remains—highly questionable. Given the complex challenges the world is facing today, and the need for Canadian institutions of higher learning to confront their colonial roots, it is time for us to critically examine this history and explicitly (re)position integrity at the core of Canada’s higher education institutions.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.042 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| 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".