Mind the Gaps: Examining Youth's Reading, Math and Science Skills Across Northern and Rural Canada*
Bibliographic record
Abstract
Abstract A new body of sociological research is finding that northern and rural youth, and in particular, low‐SES youth, face difficulties accessing higher levels of postsecondary education and lucrative fields of study such as the STEMs. However, existing research has yet to systematically measure the skills proficiencies of youth in these regions nor have we understood the factors which might account for regional differences. We draw on multiple cycles of Statistics Canada's Youth in Transition Survey, Cohort A linked to the Programme for International Student Assessment scores to investigate how location of residence impacts skills proficiencies at age 15 in math, science, and reading outcomes. Overall, our results point to three key findings. First, southern youth outperform northern youth in mathematics skills. Second, we uncover a southern (both urban and rural) and northern urban advantage in reading proficiencies. Third, in science literacy, southern and northern urban youth experience significant advantages over youth from northern rural locations. While some of the skills differences are attributable to parenting styles, parental socioeconomic status, student academics, and province of residence, they are not completely attenuated by these factors. We conclude with a discussion of the implications for future research and public policy.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| 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".