Social Justice Leadership in Urban Schools: What do Black and Hispanic Principals Do to Promote Social Justice?
Bibliographic record
Abstract
Despite the constant barrage of federal and state initiatives and reforms, many challenges to needy schools still remain. Students in the United States who are from low-income families, who are of color, and for whom English is not their first language, continue to be under-represented, undereducated, and underperform. Utilizing a qualitative research methodology, this study examined how and to what extent black and Hispanic principals working in urban schools were exercising social justice leadership in their schools, sought a better understanding of how they had become social justice leaders, and explored what they had done to promote social justice. Malgré le déluge incessant d’initiatives et de réformes de la part du gouvernement fédéral et des états, les écoles défavorisées continuent à faire face à de nombreux défis. Aux États-Unis, les élèves de familles à faible revenu, qui sont de couleur et pour qui l’anglais n’est pas la langue maternelle continuent à être sous-représentés, sous-scolarisés et moins performants. Reposant sur une méthodologie de recherche qualitative, cette étude s’est penchée sur les directeurs noirs et hispaniques d’écoles en milieu urbain pour établir comment, et dans quelle mesure, ils exerçaient un leadeurship en justice sociale dans leurs écoles; pour mieux comprendre comment ils étaient devenus des leadeurs en justice sociale; et pour étudier ce qu’ils avaient fait pour promouvoir la justice sociale. Mots clés : leadeurship en justice sociale; directeurs d’école noirs et hispaniques; équité et égalité; modélisation; éducation en milieu urbain
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".