Influential Spheres: Examining Actors’ Perceptions of Education Governance
Bibliographic record
Abstract
Many layers of education governance press upon U.S. schools, so we separated state actors into those internal to and those external to the system. In the process, we unpacked the traditional state–local dichotomy. Using interview data (n = 45) from six case-study states, we analyzed local leaders’, state-internal actors’, and state-external players’ perceptions of implementation flexibility and hindrances across several policy areas. We observed how interviewees’ spheres of influence linked to which policy areas they viewed as salient or not, and their relative emphaseson who and what within state education systems contributed to implementation flexibility and/or hindrances, and how these factors played out. We found important differences by sphere: the local sphere produced the most coherent findings, and state-internal was least coherent. We discuss implications for education governance research, applications for practitioners and policymakers, and a methodological contribution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".