Developing professional capital in policy and practice
Bibliographic record
Abstract
Purpose – The purpose of this paper is to provide evidence from Ontario’s Teacher Learning and Leadership Program (TLLP) with goals to: support experienced teachers to undertake self-directed professional development; develop teachers’ leadership skills for sharing their professional learning and practices; and facilitate knowledge exchange. Design/methodology/approach – The paper draws on four years of research including: analyses of TLLP project application proposals and final reports; observations and participant feedback for TLLP events; surveys of TLLP project leaders; vignettes by teacher leaders; interviews with teachers, government and union leaders; case studies of TLLP projects; and analyses of TLLP online networking activity. Findings – TLLP provides an example of developing professional capital, involving: changes in the style and substance of policy making concerning government and unions’ approaches to teachers’ professional development; and the experiences of teachers developing their individual and collaborative professional learning and leadership with benefits for teachers and for students. Originality/value – Five implications are highlighted: prioritizing teachers’ learning and leadership of, by and for teachers requires shifts in the substance and style of policy making for teachers’ work; professional collaboration benefits from appropriate partnerships and an enabling system with conditions and support for teachers’ learning and leadership; valuing and supporting teachers taking charge of their own professional learning is integral; developing teachers as leaders of their peers’ learning and educational improvements is vital; and enabling teachers as developers of actionable knowledge is powerful for sharing improvements in practices.
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 | 0.044 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.032 |
| Scholarly communication | 0.020 | 0.009 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 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".