Prioritization of rural youth in Nova Scotia: understanding rural school success with policy gaps
Bibliographic record
Abstract
This study describes a qualitative exploration into rural school success in Nova Scotia and the prioritization of its rural youth in educational policy. Case study methodology was used to conduct research using interview and text analysis methods. Factors of success and their key components that rural school personnel perceive as priorities in their buildings and the representation that rural school youth in Nova Scotia receive were explored from a developed conceptual framework using High-Performing High-Needs (HPHN) rural schools (Barely & Beesley, 2007; McREL, 2005a; Canada Without Poverty, 2019). This thesis describes the important roles that key components such as community support, student well-being, teacher and student retainment, performance pressures and lack of policy play in the future of rural school success in Nova Scotia. The study concludes that the lack of Canadian, more narrowly Nova Scotian, rural education policy puts rural youth at a disadvantage in their quest to obtain a quality and equitable education. Recommendations are provided to narrow this policy gap, and strategies that rural school personnel deemed effective are shared. These strategies are recommended as guidelines into best practices in policy development to ensure that youth in rural Nova Scotia, and indeed other jurisdictions, have an equitable voice in their pursuit of an equitable and quality education.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".