Unforeseen benefits: outcomes of the Qanuinngitsiarutiksait study
Bibliographic record
Abstract
Scientific publications predominantly focus on research outcomes. Increasingly, community partnerships and relationships are mentioned, especially in research conducted with Indigenous communities. In partnership-based research, Indigenous communities expect researchers to contribute in a multitude of ways that go beyond doing research. This article reports on a series of unforeseen, yet positive contributions realised in the Qanuinngitsiarutiksait study, undertaken between 2015 and 2021. These contributions are different from the main outcomes of the study. Salient unforeseen benefits included the strengthening of the Manitoba Inuit community through hosting community feasts, games, and virtual events; creating opportunities to increase the visibility of Inuit Elders at University public events; supporting the growth of the Manitoba Inuit Association in terms of staff, programmes, and presence at provincial policy tables; leveraging relationships towards the development of Inuit-centric primary healthcare services in Winnipeg; creating a method to identify Inuit in provincial administrative datasets which were used to track COVID-19 infection rates and ensure equity in access to testing and vaccines. As a result, the Manitoba Inuit Association’s visibility has increased, and Inuit Elders have become essential contributors of Indigenous knowledge at Manitoba-based events, as First Nations and Metis have been for decades. This transformation appears to be sustainable.
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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 | 0.091 | 0.158 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".