Aligning Universities Requirements of Indigenous Academics with the Tools used to Evaluate Scholarly Performance and Grant Tenure and Promotion
Bibliographic record
Abstract
The aim of this paper is to challenge the discrepancy between candidate requirements in job postings for Indigenous scholars and their recognition in the tenure-track stream. For the purposes of this article, I conducted a scan of 11 academic positions for Indigenous scholars advertised in Canada from 2017 to 2019. One-hundred percent of the postings included an expectation for the candidate to hold Indigenous Knowledges and connections to Indigenous communities. Through the examination of seminal Indigenous scholars I unearth the capacities required to hold, maintain, and renew Indigenous Knowledges and connections to community, while simultaneously showing that none of these capacities are recognized within funding allowances, workload allotments, or tenure and promotion committees. Finally, practical recommendations are offered to post-secondary institutions to provide a supportive environment for Indigenous scholars to enjoy success while holding Indigenous Knowledges and community connections in a good way. Keywords: Indigenous education; decolonizing post-secondary education; anti-oppressive education
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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 | 0.074 | 0.198 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".