Bibliographic record
Abstract
A good education is generally considered a cornerstone for getting a good job and building financial security. For many Inuit, however, the education system is another southern institution that has only recently taken hold in their lives. Inuit live in Canada’s north, primarily in one of four regions: the Inuvialuit region of the Northwest Territories, the Territory of Nunavut, the Nunavik region of northern Quebec, and the Nunatsiavut region of northern Labrador. Collectively, these regions are known as Inuit Nunaat, or “the land where Inuit live.” With land claim agreements signed in all regions of Inuit Nunaat, there are increasing opportunities for Inuit to take a role in the future of their communities and regions, but poorer educational attainment puts these possibilities out of reach for many. In this chapter we will use Census data from 1981 to 2006 to look at the educational attainment of Inuit over time. An analysis of Inuit educational attainment poses several problems, including the difficulty we have had identifying the Inuit population from Census to Census over the past 25 years. However, it is clear that no matter how one defines the Inuit population, Inuit educational attainment, particularly post-secondary education, has remained far below that of the rest of Canada.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".