Methodology to Study Teacher Agency: A Systematic Review of the Literature
Bibliographic record
Abstract
Teacher agency is a set of actions that a teacher takes beyond what is generally expected of them. The concept merits examination, as agency can bolster teachers’ ability to set and achieve professional development goals. To better understand how to study, and use, this relatively new concept in the academic literature, a systematic review of 164 publications written by researchers from 41 countries was conducted in order to document the research approaches used to study teacher agency, the participants whose agency was documented in a school setting, the methodology used and the type of analysis performed. The study found that teacher agency has been documented qualitatively in the form of case studies comprising interviews of a small number of individuals, with no consensus in terms of interview protocol. In most cases, the results are analyzed using emergent coding. The way that agency is documented varies but is most often underpinned by an ecological approach.
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 | 0.070 | 0.112 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.008 |
| Bibliometrics | 0.061 | 0.038 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 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".