Negotiating expertise in an action research community
Bibliographic record
Abstract
When teachers talk back to theories, making them “internally persuasive discourse … half ours and half someone else's” (Bakhtin, 1981, pp. 345–346), theories can be dynamic, can lead to productive dialogue and generative reflection. (Ritchie & Wilson, 2000, p. 18) Introduction Research collaboration between teachers and academics has recently been advocated not only as a means of producing good educational research, but also as a powerful form of teacher education. In this chapter, we examine some discursive episodes in the research meetings of a group of variously situated educators, a group of which we are members. The group, composed of teachers of children, a video ethnographer, graduate students, and a professor in a faculty of education, is interested in the education of children of diverse language and ability backgrounds. In this chapter, we examine occasions upon which participants in our group talk back to, create, and modify their own and others' theories and knowledge and other occasions that seem to preclude, or at least inhibit, such conversations. In so doing, we hope to understand better how to engage in collaborative research and teacher education in ways that allow for generative dialogue and critical action that leads to classrooms in which children, as well as teachers, are able to talk back and participate in dialogue with their own and others' theories and knowledge. Collaborative educational research Including differently situated educators in educational research activities has often been discussed in general educational and, more recently, in second language educational literature (Bailey et al., 1998; Cochran- Smith & Lytle, 1999a, 1999b; Freeman & Johnson, 1998; Nunan, 1992).
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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 | 0.118 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.012 | 0.053 |
| Scholarly communication | 0.021 | 0.033 |
| Open science | 0.005 | 0.035 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 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, 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".