Towards reconciliation: 10 Calls to Action to natural scientists working in Canada
Bibliographic record
Abstract
In 2015, after documenting testimonies from Indigenous survivors of the residential school system in Canada, the Truth and Reconciliation Commission released 94 Calls to Action to enable reconciliation between Indigenous and non-Indigenous Canadians. Without personal connections to Indigenous communities, many Canadians fail to grasp the depth of intergenerational impacts of residential schools and associated systemic racism. Consequently, reconciliation remains an elusive concept. Here we outline 10 Calls to Action to natural scientists to enable reconciliation in their work. We focus on natural scientists because a common connection to the land should tie the social license of natural scientists more closely to Indigenous communities than currently exists. We also focus on natural sciences because of the underrepresentation of Indigenous peoples in this field. We draw on existing guidelines and our experiences in northern Canada. Our 10 Calls to Action are triggered by frustration. The authors have witnessed examples where natural scientists treat Indigenous communities with blatant disrespect or with ignorance of Indigenous rights. These 10 Calls to Action challenge the scientific community to recognize that reconciliation requires a new way of conducting natural science, one that includes and respects Indigenous communities, rights, and knowledge leading to better scientific and community outcomes.
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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 | 0.103 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.147 | 0.075 |
| Scholarly communication | 0.030 | 0.011 |
| Open science | 0.012 | 0.033 |
| Research integrity | 0.034 | 0.054 |
| 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".