Aboriginal Employment and Wages in Canada: A Decade of Positives and Negatives
Bibliographic record
Abstract
The employment and participation rates for Aboriginals improved in 2016 over 2015, while the unemployment rate remained the same. However, Aboriginals, as well as non-Aboriginals, have not reached the 2007 levels they were prior to the recession of 2008- 2009. Wages have improved annually and in most years at a rate greater than the consumer price index. This applies for Aboriginals and non-Aboriginals, except in 2016, when wages were basically unchanged for Aboriginals. In general, the rates of employment, unemployment, participation and wages are more favourable for non-Aboriginals than for Aboriginals. However, when examined by the level of education completed, employment rates are similar. Employment and wages are examined for the previous ten years, focusing on changes in 2007, which was prior to the recession, in 2010, immediately after the recession and in 2015 and 2016. Gender, age and educational differences are discussed.
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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 | 0.000 | 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) | 0.000 | 0.000 |
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 teacher head, 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".