Bibliographic record
Abstract
The State of the Aboriginal Economy has continued to improve in 2018.Aboriginal employment and wage rates increased, and unemployment rates decreased.The unemployment rate for both Aboriginals and non-Aboriginals was at its lowest level in 2018, going back to 2007, which is the period for which the data is reported.In the following article, employment-related rates are examined for Métis, First Nations, and Inuit, and by gender, age, province, economic sector, and education level.Historically, these rates have been better for non-Aboriginals than for Aboriginals, and this is still the case in 2018.However, as in previous years, employment and wage rates are similar for non-Aboriginals and Aboriginals when measured by the education level completed.Thus, there appears to be a clear relationship between the education level completed and wage rates, as well as employment rates.A reasonable conclusion is that adopting strategies that increase education levels of Aboriginals would improve the state of the Aboriginal economy.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.389 | 0.209 |
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".