Investigating teacher leadership as a means of building school capacity
Bibliographic record
Abstract
The purpose of this research was to investigate teacher leadership as a means of building the capacity of one school to respond to the diverse learning needs of all students. This school was selected because the quantitative data collected in a larger study on impact of Secondary School Reform in Ontario schools, indicated that this school had established collaborative networks and strong relationships. It appeared that an environment had been created in this school where the expectations for teachers to work as school-wide leaders had become the norm. A shared leadership model had been established and teachers worked within that model through Management Learning Teams (MLT) to become school-wide leaders. Using a qualitative approach that included a combination of observation of teachers in action in their MLTs as well as follow-up interviews, the dynamics of how teachers work as leaders was examined. Data were gathered about the nature of the leadership activities that teachers were involved with outside the classroom, how teacher leaders influenced one another, how communities of learning were facilitated and how teachers leaders overcame the obstacles they encountered. The strongest theme that emerged is that principal and teacher leadership are inseparable as means of enhancing school capacity. Both the principal and the teachers must adopt new roles as leaders within the school and these new roles must be part of a shared leadership model that outlines the communication and flow of information to all the members of the school. Central to this model was the connection between administration as the strategic leaders of the school and the teachers as the leaders in the classroom. Creating structures that were built into the daily routines of the school empowered teachers to become involved and impact the decisions that were being made about a variety of different teaching and learning issues. However, in spite of the many opportunities for collaboration it became evident that professional autonomy still seems to play a powerful role in limiting the degree to which teacher leaders could influence a change of instructional practice that may lead to improved student learning.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".