Accomplished Education Leaders' Perspectives on Competition, Capacity, Trust, and Quality
Bibliographic record
Abstract
From 2017 to 2019, the primary strategy to improve public schools in the U.S. was increasing competition through the expansion of charter schools and the promotion of vouchers to send public school students to private schools. The problem this presented was that key education leaders had not provided adequate input and feedback into this strategy. The purpose of this qualitative study was to gather the perspectives of accomplished education leaders on how Tiebout's theory of competition and the concept of the Ontario K-12 School Effectiveness Framework impacted quality, trust, and capacity. Data were collected using semistructured interviews with a purposeful sample of 15 accomplished education leaders from the charter/school choice community and traditional public schools. Data were analyzed using Bernauer's modified three-phase method. School and classroom leadership, meaningful and informative assessment that guides instruction, substantive student engagement, and a focus on a strong curriculum and effective teaching were the key themes that aligned with quality, trust, and capacity. Education leaders did not see Tiebout education as a key driver that would alone improve the quality of public education. Leaders believed that some schools improved in response to Tiebout competition but also shared cautions on the diminishing returns, collateral damage, and equity concerns because Tiebout competition created winners and losers. Social change may be impacted by the results of this study in that the results define and share examples of healthy and unhealthy competition in public education. The results of this study can help inform policy makers and educators as they create opportunities that will enhance the long term personal and economic success of all U.S. students.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".