Enhancing Academic Achievement for Students Living in Poverty Through Leadership and Professional Learning Communities (PLCs)
Bibliographic record
Abstract
With the persistent increase in the number of students living in poverty coupled with community pressure to improve academic achievement for all learners, educational leaders have been pursuing initiatives to enhance success for all their students. This researcher surveys the literature on the correlation between academic achievement and socio-economic status in twenty-five elementary inner city schools in Toronto. The study examines leadership and professional learning communities (PLCs) in high performing schools serving students from poor communities. The mixed methods sequential approach was applied. Stratified sampling strategy was used to collect Education Quality and Accountability Office (EQAO) data on grade three students in reading, writing and mathematics for three consecutive years in order to identify high performing schools. Teacher questionnaires were administered to explore the impact of leadership and professional learning communities (PLCs) in developing instructional practices. Information collected from principals' interviews explores leadership practices that support educational achievement in high performing schools. Results from the findings will support schools in their attempts to accomplish enhanced academic achievement for all learners.
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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 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".