Building Sustainable Civic Capacity in Urban Education Reform: Actors, Perceptions, and Recommendations for Inclusive Public Policy
Bibliographic record
Abstract
The research and literature focusing on education change, policy development, and community engagement have indicated clearly that forming a broad coalition within urban education reform through greater civic engagement can create sustainable education change, help to develop inclusive education policy, and lead to greater accountability, transparency, equity, and efficacy in delivering 21st-century education to all students. However, the actors, barriers, and opportunities related to developing inclusive educational policy and greater civic capacity in urban education reform have been under-examined in the literature around public policy and civic engagement. Drawing from quantitative and qualitative data collected in a concurrent triangulation mixed-methods study, this article examines the perceptions and relationships of various actors in urban education reform in Chicago, Illinois, and Milwaukee, Wisconsin, and offers a deeper understanding of the barriers to and opportunities for fostering greater civic capacity and engagement in urban education reform, and developing inclusive educational policy. The study findings suggest strongly that sustained civic capacity and engagement in urban education change efforts allow for systematic improvements in educational development and innovation. Moreover, the results indicated that structural openness to new actors, stakeholders, and the reconceptualization of education as a worthy good can lead to enhanced educational quality, equity, and inclusion, particularly in urban areas. The authors also present further discussion about and policy recommendations for increased civic engagement in urban school reform efforts.
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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 | 0.017 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.019 |
| Scholarly communication | 0.020 | 0.010 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| 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".