Bibliographic record
Abstract
Teacher leadership is an increasingly significant area of inquiry in education, yet it remains conceptualized in diverse and sometimes inconsistent ways across contexts.In this special issue, eight articles examine how teacher leaders influence school and system improvement through their leadership, often without formal titles or designated roles.The variation in terminology used across the articles reflects broader challenges in the field: while the phenomenon of teacher leadership is widely observed, it is described in multiple ways that may hinder coherence and accessibility of the research literature.A recurring theme across the contributions is the role of teacher leaders as influencers and professionals who lead change through expertise, relational work, and collaborative practices, rather than through positional authority.While some articles explore the work of teachers who take on responsibilities commonly associated with "middle leaders" (Edwards-Groves, Grootenboer, Tindall-Ford, & Attard, 2025) and those who support colleagues' professional learning within schools, notably none of the authors use this term.This signals both a gap and an opportunity: the need to clarify and converge on terminology to enhance scholarly dialogue and knowledge mobilization.This special issue aligns with recent scholarship that conceptualizes leadership as influence and relational capacity rather than formal authority or role (Hargreaves, 2023).
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.230 | 0.130 |
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".