I’ve Got You Covered: Adventures in Social Justice-Informed Co-Teaching
Bibliographic record
Abstract
What is social justice-informed co-teaching? Why is it important? How can it enrich social justice pedagogy? While the answers to these questions may vary depending on context and perspective, they are nevertheless useful to address. Each of these questions will be discussed in this research paper. This auto-ethnographic narrative inquiry adds to the literature on social justice-informed co-teaching in an innovative way. It critically examines the purposeful endeavor of two professors who used social justice thinking to guide their co-teaching practice, and simultaneously used co-teaching to enrich their social justice pedagogy. At once, this paper is a lived experience, a story, and a research study. In deconstructing two narratives, the authors articulate specific ways in which co-teaching, as a practice, presents unique opportunities for social justice learning. Implications for research and practice in teacher education programs, teaching practices and field- experiences, and co-teachers themselves are shared in the closing segment of the paper.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.027 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.008 |
| 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".