Returns to education and occupations for Canadian Aboriginal people
Bibliographic record
Abstract
Purpose The purpose of this paper is twofold. First, drawing on a unique data set, the authors estimate the returns to education for Canadian Aboriginal people. Second, the authors explore the relationship between occupation and the economic well-being, measured as income, of Aboriginal people in an effort to provide a better understanding of the causes of income gaps for Aboriginal people. Design/methodology/approach The data used in this study is the Public Use Microdata File of Aboriginal People’s Survey, 2012. An ordered logit model is used to estimate the key determinants for income groups. Then the marginal effects of each variable, for the probability of being in each category of the outcomes, are derived. Findings All the explanatory variables, including demographic, educational and occupational variables, appeared statistically significant with predicted signs. These results confirmed relationships between income level and education and occupations. Research limitations/implications The data limitation of income, as a categorical variable prevents the precise estimation of the contributions of the dependent variables in dollar amount. Social implications In order to substantially improve the Aboriginal people’s market performance, it is important to emphasise the quality of their education and whether their areas of study could lead them to high-skilled occupations. Originality/value Attention is paid to the types of human capital rather than the general term of education.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".