Liderar los aprendizajes: acciones concretas en pos de la mejora escolar (Leading learning: concrete actions in pursuit of school improvement)
Bibliographic record
Abstract
In order to handle the role, incidence and evolution of school leadership in recent years, Michael Fullan focuses his analysis on three fundamental themes: leading learning, creating coherence and facilitating the teaching professional task. From this perspective, the author highlights some key aspects for the Chilean context. Especially, he justifies, from the evidence, the value of making the greatest possible efforts to concentrate the leadership function in the improvement of teaching practices.ResumoA fim de dar conta do papel, incidência e evolução da liderança escolar nos últimos anos, Michael Fullan concentra sua análise em três temas fundamentais: liderar a aprendizagem, criar coerência e facilitar a tarefa profissional docente. Nessa perspectiva, o autor destaca alguns aspectos fundamentais para o contexto chileno. Especialmente, justifica a partir de evidências o valor de fazer os maiores esforços possíveis para concentrar a função de liderança na melhoria das práticas de ensino.ResumenCon el objetivo de dar cuenta del papel, incidencia y evolución del liderazgo escolar en los últimos años, Michael Fullan centra su análisis en tres temáticas fundamentales: liderar los aprendizajes, crear coherencia y facilitar la tarea profesional docente. Desde esa óptica, el autor subraya algunos aspectos claves para el contexto chileno. Especialmente, reivindica desde la evidencia el valor de hacer los mayores esfuerzos posibles por concentrar la función directiva en la mejora de las prácticas de enseñanza.Keywords: School leadership, Michael Fullan, Teacher, Teaching practices.Palavras-chave: Liderança escolar, Michael Fullan, Docente, Práticas de ensino.Palabras claves: Liderazgo escolar, Michael Fullan, Profesor, Prácticas de enseñanza.ReferencesAUSTRALIAN INSTITUTE FOR TEACHING AND SCHOOL LEADERSHIP, AITSL. Principal Leadership Standards. Melbourne: Autor, 2016.BREAKSPEAR, S.; PETERSON, E.; ALFADALA, A.; KHAIR, S. Developing agile leaders of learning. Abu Dhabi: WISE, 2017.ELMORE, R. School reform from the inside out. Cambrindge, MA: Cambridge University Press, 2004.FULLAN, M. Indelible leadership: Always leave them learning. Thousand Oaks, CA: Corwin Press, 2017.FULLAN, M.; HARGREAVES, A. Bringing the profession back in: A call to action. Oxford, OH: Learning Forward, 2016.FULLAN, M.; QUINN, J. Coherence: Putting the right drivers in action. Thousand Oaks, CA: Corwin Press, 2016.HARGREAVES, A.; FULLAN, M. Professional capital: Transforming teaching in every school. Nueva York: Teachers College Press, 2012.HATTIE, J. What works best in education: The politics of collaborative expertise. Londres: Pearson, 2015.LEANA, C. The missing link in school reform. Stanford Social Innovation Review. V. 9, No. 4, 2011, pp. 30-35.PONT, B. The case of school leadership reform in comparative perspective. Artículo presentado en la reunión anual de la ASOCIACIÓN DE INVESTIGACIÓN EN EDUCACIÓN, 2017.ROBINSON, V. Student-centered leadership. San Francisco: Jossey-Bass, 2011.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".