Employing Gadamerian dialogue for teacher professional development in a democratic society
Bibliographic record
Abstract
Understanding curriculum shifts its focus from “the separation of subject and object” to their negotiation, integration, and dialogue (Pinar et al. 1995, p. 502). Doll (1993a) notes that in the process of meaning making, the paradox of subject-object, or teacher-learner makes sense only once one is included in the other. Learning experience is created, facilitated, and supported by dialogue. One needs the other for one’s own sense of being, becoming, thriving and transforming. As Doll notes, dialogue in curriculum focuses on the process of “traversing the courses of negotiating with self and others” (p. 286). Teachers become a new subjectivity as they learn from each other in dialogic conversations to transform their understanding of self and educational experience. My theoretical study specifically focuses on Gadamerian (1990) dialogue using which teachers fall into a genuine conversation during which no one knows in advance what will “come out” of a conversation (cited in Clarke, 2012, p. 60). As I find this understanding of dialogue as spontaneous, generative, transformative, and emancipatory, I will inquire into how Gadamerian dialogue can contribute to teacher professional development in a democratic society.
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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 | 0.019 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.055 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".