Coherent school improvement: Integrating outcomes-based assessment and trauma-informed practice
Bibliographic record
Abstract
This literature review tests a framework for coherent implementation of school improvement initiatives. Often in education, initiatives are introduced as disparate, isolated approaches towards improved student learning. As a result, teachers, school-based administration and school districts frequently change their focus, contributing to fragmentation, stagnation and initiative fatigue. Robinson et al. offer ‘five domains of organizational activity’ as key areas of focus for coherent school improvement. We investigate application of Robinson et al.’s five domains to two seemingly disparate school improvement initiatives: outcomes-based assessment (OBA) and trauma-informed practice (TIP) as both represent significant areas of focus in our context. We construct our literature review around the central question: Can two divergent aspects of school improvement: outcomes-based assessment and trauma-informed practice, be aligned through Robinson et al.’s five domains, to coherently support their integration in schools? We found that Robinson et al.’s five domains were a useful tool for alignment of these diverse initiatives and were able to extrapolate beyond application to OBA and TIP, to other school improvement initiatives. Coherence benefits administration, teachers, and most importantly, promotes student achievement. When all elements of school improvement are part of a cohesive whole, all members the school community are better able to understand their role in driving 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 | 0.137 | 0.164 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.019 | 0.012 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".