Working with us, not for us: strategies for being a better ally to First Nations people
Bibliographic record
Abstract
We write this article together – Kelly, a First Nations woman living on Kombumerri Country, and Richard, a Canadian white male settler living on the lands of the Minjungbal people of the Bundjalung Nation. As a First Nations Australian academic, Kelly is often approached to give guest lectures. She aims to accept these invitations as she believes acts of reciprocity and relationality are essential building blocks for reconciliation. Further, her job requires her to teach First Nations People’s histories and knowledges. Unfortunately, on many occasions, her knowledge is appropriated, reproduced without permission, frequently misconstrued, or misrepresented and colonised in some way. This all happens under the guise of a non-Indigenous person having “good intentions”. In addition, Kelly is frequently micromanaged regarding her Indigenous knowledges. This is not an uncommon experience for First Nations academics.
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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 | 0.019 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.050 | 0.026 |
| Scholarly communication | 0.020 | 0.023 |
| Open science | 0.003 | 0.029 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".