Bibliographic record
Abstract
Urban schools in high-poverty communities face unique challenges. It is often the school principal who is tasked with addressing achievement gaps, low scores and students with high needs. Despite the importance and the difficulties of their role, the voices of many of these dedicated leaders are not often heard. This narrative inquiry shares the insights of two elementary principals in urban schools who recount the barriers, supports and rewards of their role. Using moral leadership as a theoretical framework, the findings of this study include a call for school boards to consider carefully the qualities and passions of their leaders when assigning principals to urban schools. Keywords: principal, moral leadership, high-poverty, urban schools, narrative inquiry Les écoles en milieux urbains avec des taux élevés de pauvreté font face à des défis uniques. Il revient souvent aux directeurs d’école de répondre aux écarts en matière de rendement, aux faibles résultats et aux élèves ayant des besoins élevés. Malgré l’importance et les difficultés de leur rôle, les voix de plusieurs de ces leaders dévoués ne se font pas souvent entendre. Cette enquête narrative présente les perspectives de deux directeurs d’écoles primaires urbaines qui racontent les obstacles, les supports et les récompenses qui les accompagnent dans leur rôle. Reposant sur le leadership moral comme cadre théorique, les résultats évoquent, entre autres, le besoin pour les conseils scolaires d’examiner soigneusement les qualités et les passions de leurs leaders lors de l’affectation des directeurs dans les écoles urbaines. Mots clés : directeur, leadership moral, taux élevé de pauvreté, écoles urbaines, enquête narrative
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.012 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| 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".