Schools of Excellence AND Equity? Using Equity Audits as a Tool to Expose a Flawed System of Recognition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The purpose of this article is to demonstrate how equity audits can be used as a tool to expose disparate achievement in schools that, on the surface and to the public, appear quite similar. To that end, the researcher probed beyond surface-level performance composite scores into deeper, more hidden data associated with state-recognized "Honor Schools of Excellence." How is "excellence" defined and operationalized in these schools? Are these schools "excellent" for all students? Can a school really be classified by the state as "excellent" and yet still have significant "gaps" and disparities? If so, is the state's formula used to identify exemplary schools too simple, dogmatic, and institutionally flawed? Through the use of equity audits, quantitative data was collected to scan for systemic patterns of equity and inequity across multiple domains of student learning and activities within 24 elementary schools. The intent was to document and distinguish between schools that are promoting and supporting both academic excellence (small gap schools; SGS) and systemic equity and schools that are not (large gap schools; LGS). Results reveal that although demographic, teacher quality, and programmatic audits all indicated a fair amount of equity between SGS and LGS, the achievement audit between both types of schools indicated great disparities. By controlling for or eliminating some of the external variables and internal factors often cited for the achievement gaps between white middle-class children and children of color or children from low-income families, the findings from this study raise more questions than answers. Results do indicate that equity audits are a practical, easy-to-apply tool that educators can use to identify inequalities objectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it