Inspiring Creativity in Urban School Leaders
Bibliographic record
Abstract
This paper presents an analysis of how guided engagement with the arts can provide leadership lessons for school leaders and administrators. The study was conducted as part of two projects funded by the School Leadership Program (SLP) grants from the U.S. Department of Education. The principal interns and practicing school leaders participated in arts engagement activities (jazz ensemble, chamber orchestra, and tango dance) facilitated by teaching artists from the Maxine Green Center for Aesthetic Education and Social Imagination. Participants attended experiential workshops with teaching artists, observed the art form and then listened to the process and techniques used by the artists. Data sources for the study included observations, reflective narratives and interviews with participants. These were analyzed using grounded theory methods. The findings indicate that guided engagement with the arts provide lessons to school leaders in the form of interdisciplinary analogies and metaphors. The narratives generated by artists and participants served as a bridge: building connections between leadership and artistic practice. The experience encouraged participants to: gain new perspectives on optimal contexts for learning, develop a nuanced understanding of leadership, move from abstract to concrete understanding of relational constructs, and feel empowered through trying new experiences. Implications of the findings, including translating the lessons into actual practice and the addressing the needs of participants who did not connect with the sessions, are also discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| 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 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".