Responding to the Call for Educational Justice
Bibliographic record
Abstract
The work presented in this volume attests to the innovative and successful educational alternatives designed and implemented by Catholic religious groups to improve educational, career, and life outcomes for urban children, adolescents, and adults placed at risk. These efforts have helped thousands of urban citizens break away from the chains of poverty and poor academic preparation to succeed in high school and beyond and secure a place of meaning and influence in adult society. In this volume, we examine the contributions of networks of schools, such as NativityMiguel and Cristo Rey schools in the U.S. and Canada and Fe y Alegría based in South America and operating in multiple countries, as well as more local initiatives. There is much to be learned from these initiatives that can improve urban education and this edited volume provides this opportunity to educators, planners, funders, and others who are inclined to invest in effective urban education.The perspectives taken in these chapters include current approaches to critical race theory, faith perspectives that promote justice, and the building of social capital and resilience to succeed academically despite considerable adversity associated with economic poverty. The chapters included here explore educational structures that communicate high expectations for student and teacher performance and provide individualized instruction, caring mentoring, and support beyond graduation in order to help develop men and women of confidence, skill, leadership, and integrity and ensure high levels of success in a world that tends to exclude them more than welcome them.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".