Reflecting on the “10 Calls to Action to Natural Scientists” 5 years later: how do we keep moving forward on reconciliation?
Bibliographic record
Abstract
Five years ago, we published a paper that proposed 10 Calls to Action aimed at enabling reconciliation in the natural sciences research arena in Canada. Our goal was to provide a starting point for those aiming to build reconciliation with Indigenous peoples and communities into science. The 10 calls were inspired by the Truth and Reconciliation Commission of Canada's 94 Calls to Action. There was, and remains, a demand for such guidance as indicated by the paper's 74 000 (and growing) downloads; it is currently FACETS most downloaded paper. As a group of Indigenous and non-Indigenous authors, we now reflect on progress made as well as ongoing challenges and opportunities. Our reflections are framed by four questions posed by the late Mazina Giizhik-iban, Murray Sinclair: 1) Where did we come from; 2) Where are we going; 3) Why are we here; 4) Who are we?, and stem from an overview of federal government initiatives, document analysis of university strategic plans, and experiences leading dozens of question-and-answer sessions on the original paper and a follow-up film. We identify a need for personal engagement and the centering of Indigenous self-determination in research, and propose two new calls to action.
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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 | 0.056 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.033 | 0.056 |
| Scholarly communication | 0.033 | 0.028 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.023 | 0.066 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".