Bibliographic record
Abstract
On behalf of our publisher, editorial board, and staff, we are excited to share the research and the stories of problems and solutions that are con tributing to a strong Indigenous economy in Canada.Thank you to all our contributors.In the Lessons from Experience, the achievements of a variety of Indigenous entrepreneurs are highlighted -from the Cando winners to those engaged in agriculture and sustainable energy.Supports include a chamber of commerce and a program for recapturing driver's licences.These themes continue in the rest of the volume, with contributions focused on important employment statistics, the need for support pro grams in Quebec, and the New Brunswick vision for healthy Indigenous communities.Although the stories in this issue reflect the time before COVID, the lessons remain valuable, and the hope in these pages must not be forgot ten.COVID-19 has struck the Canadian Indigenous economy hard.The future of the Indigenous economy in Canada will be based on how this storm is weathered and what support local Indigenous businesses will receive.So we urge you to support your local Indigenous businesses, and to stay safe.
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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 | 0.018 | 0.116 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.017 | 0.015 |
| Insufficient payload (model declined to judge) | 0.108 | 0.067 |
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".