Teacher Inquiry Groups as Professional Development: Working Towards Social Justice and Meaningful Change
Bibliographic record
Abstract
The research discussed in this paper is part of a large national study. Teachers at multiple sites across the country are engaging in Teacher Inquiry Groups (TIGs) where the participants’ are using postcolonial literature to teach for social justice. The TIGs are used as a means for participants to build their curricular and pedagogical knowledge, engage in critical self- and professional reflection, and enhance their capacity to achieve their social justice and equity goals. We report on one research site where data were collected through audio-recordings of monthly TIG meetings, individual interviews with participating teachers, teacher logs, researchers’ field notes, and focus group interviews with students who engaged with the literature. Themes constructed from our critical comprehensive analysis of data will be introduced in our presentation. In recent years, professional learning communities (PLCs) have become a “hot topic” in the field of education (Stoll, et al., 2006), presented as providing greater opportunities for teachers’ professional development than earlier models. Although we ultimately argue that the unique structure of TIGs can promote change in teachers’ curricular and pedagogical knowledge, we also offer a constructive critique exposing the limitations of the TIG process.
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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 | 0.098 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.020 | 0.047 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.004 | 0.028 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".