Conventional Wisdom and Popular (Mis)Understanding of “Failing Schools”
Bibliographic record
Abstract
While the conception of “failing schools” has proliferated the American public’s (mis)understanding of performance of low-income urban public schools unabated since A Nation at Risk, education literature suggests “failing schools” are not what has been commonly described in conventional wisdom. Here, I begin by conceptualizing conventional wisdom before describing the popular narrative affixed to “failing schools” and briefly discussing the history of standardized assessments implemented to identify “failing schools”. And as the popular conception of “failing schools” position such schools as irredeemable education spaces as evidenced by students’ performance on standardized tests, in this article I attempt to posit a rebuttal to that conventional wisdom, namely that “failing schools” do not exist as isolated institutional sources of academic “failure” but are byproducts of longstanding economic and policy neglect of poor urban communities by policymakers, yet serve as convenient targets for the ultra-wealthy to divert popular attention away from systemic economic inequities which created illusion of “failing schools” to begin with.
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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 | 0.022 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.011 | 0.152 |
| Scholarly communication | 0.015 | 0.025 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.009 | 0.018 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".