Emergence in School Systems: Lessons from Complexity and Pedagogical Leadership
Bibliographic record
Abstract
The theoretical framework for this study draws on conceptual advances from two bodies of scholarship: 1) complexity thinking in education, which has recently focused on school system change and, 2) school leadership research, which has recently attended to the effects of leadership interventions to school improvement. Using a complexity-thinking framework, the purpose of this study was to understand how leadership practices contribute to shaping change in school systems and how change occurred across the system. Our study was conducted in an urban centre in Alberta within a public-school jurisdiction and in an area of the city that had a high population of students from culturally and linguistically diverse backgrounds from low-income households compared to other areas across the school jurisdiction. Students in this area typically scored in the lowest quartile on provincial standardized examinations. Our findings are significant because complexity thinking in the context of school leadership has not received sufficient empirical attention. In our study we identified and described pedagogical leadership practices that play a central role in redressing disparities currently found in schools.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.030 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".