Bibliographic record
Abstract
Nunavik, the northern Québec region of Inuit Nunangat, had stronger labour market performance than the other three Inuit Nunangat regions between 1996 and 2011. For example, Nunavik's employment rate was 54.1 per cent in 2011, while the aggregate employment rate for Inuit Nunangat excluding Nunavik was only 42.9 per cent. Nunavik enjoyed this higher employment rate despite the fact that its Inuit population had, on average, 0.2 fewer years of schooling than Inuit Nunangat as a whole. In this paper, we examine a number of factors that could explain this paradox. Of all the factors examined, (1) public sector job provision and (2) child care availability and cost appear to have the most important impact on Nunavik’s labour market outcomes. First, Nunavik’s public sector, representing two-thirds of the experienced labour force, is a more important component of the overall economy than the public sector in the other three Inuit Nunangat regions, where it represents approximately half of the experienced labour force. Second, due to the implementation of the First Nations and Inuit Child Care Initiative and the Québec Government's family policies in the late-1990s, Nunavik has the greatest availability of child care services and the lowest daily child care fee of the four Inuit Nunangat regions. Both the ample supply of child care and the low cost have contributed to large increases in female labour force participation since 1996 (7.4 percentage points).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| 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 | 0.000 |
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 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".