Bibliographic record
Abstract
Predictors of long-term academic outcomes among Naskapi adolescents from Kawawachikamach, a reserve in Northern Quebec, were examined. Adolescents from this community as from other First Nations communities are statistically at risk for high school drop out and lower levels of educational attainment. However, efforts by communities during the past two decades to narrow the gap in educational attainment between First Nations students and the general population has resulted in significant gains for students who live on reserves. In a series of three initial visits that spanned a year, adolescents from grades 6 through 11 completed a battery of measures used to assess a broad range of components of social competence. Intelligence and school grades were used as markers of the academic success; social perspective coordination and attachment to father, mother, and peers as indices of social adaptation; and identity with Native culture as an index of cultural identity. The follow-up indicators of academic success were based on school records of graduation from high school and of admission and entrance into post-secondary institutions. As expected within the academic domain, both intelligence and school grades contributed to long-term academic achievement. Within the social domain, social perspective coordination skills and attachment to father, but not attachment to mother or peers, promoted academic achievement. Within the cultural domain, lower identification with Native status was associated with school achievement. These findings highlight the need to promote both social and academic competence in the quest to further improve rates of high school graduation and post-secondary enrolment. The link between attachment to father and academic success also suggests that educational interventions should be focused on the family as well as the child. With regard to educational planning, the government-mandated curriculum should be adapted so that the Naskapi and majority education can be integrated in complementary ways rather than being pitted against each other in a way that allows for success on only one. Although each First Nations community is unique with regard to history, culture, language, and educational values, the identification of multiple predictors of academic success among Naskapi adolescents is likely relevant to other communities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".