Teacher leadership in schools: a study into the antecedents and nature of teacher leadership in schools
Bibliographic record
Abstract
The 1990s in Newfoundland and Labrador were particularly turbulent for education. In the midst of a provincial Royal Commission calling for reform in education and a provincial government struggling to manage its finances in the face of a staggering public debt, a province-wide school improvement process initiative was instituted in 1995 with financial help from the federal government. Two responses occurred. For some, the process became a critically important way to cope with reductions in resources while maintaining programmes, while for others the process made them able to cope and develop novel and exciting programmes. This thesis is about the above factors in relation to six rural schools in one school district. The study examines the nature of teacher leadership and then looks at the antecedents and influences on teacher leadership in these schools. This study is unique in that it looks at teacher leadership as it occurs largely outside the traditional teacher leadership roles such as department heads. These schools had few such roles, thus the perspective of teachers as leaders, teachers as followers and principals reflect a genuine style of collegially based and peer recognised forms of teacher leadership. As part of a comprehensive qualitative study of six schools noted for teacher leadership, 28 respondents were interviewed. Teacher leadership often involved the mutual influence between teacher leaders and principals. These influences are discussed from the perspectives of each of the school's gender, seniority, and respondent status as teacher leader, teacher nominator, and administrator. Furthermore, these schools were developed into three models based on unique features found that were more typical of some of these schools as compared with others. While much is to be learned from this study, still more can be learned through further research. The relationship of gender to such roles could be more fully explored. In addition, what are the consequences for teacher leaders who move to other schools? Will they re-establish themselves as leaders? Questions around the role of those replacing teacher leaders that have left would also be worthy of further study. These are just a few questions that my study raises that warrant further exploration.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".