REFLECTING ON EVIDENCE: LEADERS USE ACTION RESEARCH TO IMPROVE THEIR TEACHER PERFORMANCE REVIEWS
Bibliographic record
Abstract
The paper reports on an action research (AR) project with six public high school leaders (reviewers) who volunteered to engage in an 18 month project to overcome their own defensiveness in addressing concerns with teachers (reviewees) whose performance they were evaluating. In the paper I outline how I acted as a coach in a long-term development approach where participant ownership of focus, data collection, analysis and interpretation was given highest priority. An exploration of the AR approach adopted, and the theory and strategies for addressing concerns is provided. The strategies may likely be a new, unique, contribution for many reviewers. A transcript of one reviewer-reviewee discussion sets the scene for an outline of reviewer tracking of their implementation strategies for improvement and subsequent evaluation. The final part of the paper covers a meta-level discussion of outcomes associated with the overall evaluation findings. Positive outcomes were shown for four of the six leaders for enhanced employment of strategies.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.736 | 0.850 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.016 | 0.010 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.034 | 0.028 |
| Open science | 0.010 | 0.020 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".