Building Bridges for School Improvement A Model for Sustainable University-School Partnerships
Bibliographic record
Abstract
Public education in the 21C presents high needs schools with significant challenges and often unequal access to digital technologies.To address these concerns and build bridges across this digital divide, our research team conducted a one-year pilot project to develop a sustainable university-school partnership between our faculty of education and two local high needs elementary schools.The project is examined through several lenses, including the importance of university-school partnerships, advantages and challenges of the project, sustainability, leadership in high needs schools, school improvement factors, the role of community involvement and the effect of comprehensive school health on student achievement.In this article, we describe the initial formation of a partnership between the university and two schools identified as high needs by Educational Quality and Accountability Office scores, as well as low SES and demographics indicating low levels of educational aspiration and achievement.University professors, administrators, classroom teachers, students and preservice teacher candidates, worked collaboratively to lay the groundwork for a research-based and sustainable partnership by bringing the resources, strengths, skills and expertise of the schools and the faculty of education directly to bear on the "digital divide" experienced in these schools and to provide collaborative support to improve student achievement.
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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 | 0.019 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.015 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.003 | 0.027 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".