Education in Inuit Nunangat: A Quantitative Examination of Factors Contributing to the Educational Attainments of Inuit
Bibliographic record
Abstract
The literature identifies that Inuit lag behind the non-Inuit population in terms of education credentials. The gap in the rates of post-secondary education diplomas and degrees are even greater. Studies indicate that the lack of educational achievement by Inuit is mainly linked to the emergence of residential schools and its long-lasting destructive impacts in the Arctic. Moreover, the literature suggests that some deficiencies in Inuit Nunangat such as the absence of a university and lack of post-secondary education programs affect Inuit’s participation in advanced education. This research study uses data from the 2012 Aboriginal Peoples Survey to see how factors such as age, place of residence, the involvement of family members in education, first language learned in childhood, and residential school attendance contribute to the rate of attainment by Inuit in post-secondary education. The findings of this study reveal that, by age, the likelihood of having university education increases. Inuit who living outside of Inuit Nunangat are more likely to have post-secondary credentials. The involvement of family members in student education enhances the likelihood of having higher education achievements. Those Inuit who learned English as their first language in childhood are more likely to have completed advanced education. Finally, having the experience of attending residential schooling by the student or family members increases the likelihood of having university-level credentials.
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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 | 0.002 | 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) | 0.002 | 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".