Institutional complexity and multiple accountability tensions: A conceptual framework for analyzing school leaders’ interpretation of competing demands
Bibliographic record
Abstract
While many studies on external accountability forms have illustrated the impact on the prevailing conceptions and values about the nature of school organizations, still little is known about the active role of school leaders as sense-makers who deal with conflicting accountability demands. We argue that while multiple external accountability forms driven by policies often manifest in apparently conflicting ways in school organizations, recent findings suggest that some school leaders have come to understand and adapt strategically and reconcile these logics in practice over time. In this article, we seek to highlight the institutional complexity that school leaders face when attempting to make sense of, interpret reconcile and/or counterbalance competing accountability demands from multiple and incompatible logics while considering their schools’ needs and conditions. We develop a conceptual framework that unpacks the intersection of the institutional complexity triggered by multiple institutional logics and school leaders’ sense-making about reform. This framework could illuminate how and why the multiple logics in the institutional environment shape and are being shaped by school leaders’ sense-making in the complex policy implementation processes that lead to different policy outcomes.
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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 | 0.020 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.009 | 0.071 |
| Scholarly communication | 0.019 | 0.026 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.005 | 0.005 |
| 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".