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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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; both teacher heads agree on what is shown here.
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".