Senior administrators’ perceptions of the impact of educational bureaucracy on school efficacy
Bibliographic record
Abstract
This study explores the question of how educational bureaucracies impact school efficacy, from the perspectives of senior administrators, a group made up of superintendents and assistant superintendents. The literature review defines the terms educational bureaucracy and systems coupling, which provide a theoretical framework for the study and serve as a lens through which the data, anecdotal reports contextualized by real-life experiences, are analyzed and the theme of school efficacy is explored. Understanding the unique perspective of the senior administrator’s role with regards to educational bureaucracy and its impact will provide a basis from which the structure of school systems is explored more deeply, and the ways in which systems coupling and elements of bureaucratic structures might be used as tools to improve school efficacy. The aim of this study is to better understand the specific functions of educational bureaucracies that have a perceived and/or measured effect on school efficacy. Rather than using a uniform measure of school efficacy, which could serve to limit the experiences shared by participants, the secondary aim of this study is to develop possible definitions/conceptualizations of school efficacy based on the anecdotal reports provided by participants, through the application of grounded theory. The findings of this study and the implications for practice will be of interest to those studying the sociological foundations of education and to stakeholders who wish to know more about the functioning of educational bureaucracies at the systemic level, and how they impact school efficacy.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".