Commentary: Teachers’ Professional Development: A Vital Lever for Change
Bibliographic record
Abstract
In this interview, Megan Webster describes key tenets of teachers’ professional development. First, it needs to be practice-focused, meaning that the subject is very close to what teachers do every day. Secondly, teachers need to have opportunities to examine expert forms of practice; teachers need models of excellent performance. Finally, teachers must be able to practice and receive feedback in a supportive environment with peers who work in the same subject. This model, described as the "cycle of investigation and enactment," is best implemented in a sustained, long-term way. This model allows teachers to build relationships, develop trust, and commit to improving their teaching practice. She concludes by stating that high-quality professional development for teachers is "one of the greatest levers for change that we have."
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 | 0.011 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.059 | 0.086 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".