Size, Structure, and Change: Exploring the Sources of Aboriginal Earnings Gaps in 1995 and 2005
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
I investigate the trends and sources of the Aboriginal/non-Aboriginal annual earnings gap and the on-reserve annual earnings penalty. Three sources of these gaps are considered: differences in weeks worked, weekly earnings due to characteristics, and returns. I find that earnings differences between non-Aboriginals and Métis have declined due to convergence in the number of weeks worked. The on-reserve weekly earnings penalty has increased, possibly due to the changing proportion off-reserve. My study also examines the importance of taxes and transfers in reducing the earnings gap. While they do not eliminate most inequities, measured taxes and transfers eliminate the on-reserve earnings penalty for women.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 it