Bibliographic record
Abstract
tradition of carrying out Aboriginal-related economic research, raising awareness about Aboriginal peoples, businesses and communities. This report represents our third in the series of articles on Aboriginal social and economic issues. In this report we attempt to put to bed ten myths surrounding Canada’s Aboriginal population. The myths were chosen on the basis of misconceptions we encountered while carrying out the research on our previous reports. We also sought insight from organizations like the Canadian Council for Aboriginal Business (CCAB) which have community and business reach. The misperceptions put to rest are broad-based, including: access to free post-secondary education, taxation exemption rules, and the prevalence and success of Aboriginal-owned small businesses and economic development corporations. In celebration of National Aboriginal Day on June 21st, TD Economics continues its tradition of carrying out Aboriginal-related economic research, raising broader awareness about issues confronting Aboriginal peoples, businesses and communities. This report represents our third in the series of articles. The first concluded that the tide had shifted in the right direction for Aboriginal peoples and there was a renewed spirit of entrepreneurship in the air. In our second article, we noted that Aboriginal people
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.446 | 0.162 |
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".