Mapping Research in Teacher Education on Diversities and Inequalities: Opening Possibilities Through Social Cartography
Bibliographic record
Abstract
This article considers the potential of the methodology of social cartography to open generative possibilities in research on diversities and inequalities in teacher education in the international context. Research in teacher education focusing on difference or diversities and inequalities offers highly diverse practices and orientations, yet we have found that intelligibility across research communities can be challenging and ultimately limiting for the field. Social cartography is a methodology that attempts to address this issue, inviting researchers and practitioners to create forms of conversation that are more tentative, self-critical, and generative. In this article, we introduce our priorities in teacher education that center awareness of social-cultural commitments and assumptions, as well as historical context. We then share a social cartography of teacher education research we have created to reveal the possibilities of social cartography for teacher education, as well as an invitation to open needed dialogue amongst teacher education researchers and practitioners.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".