Listening to Children in Dialogue. A Response to “‘State Your Defense!’ Children Negotiate Analytic Frames in the Context of Deliberative Dialogue”
Bibliographic record
Abstract
In this appreciative response to Jennifer Hauver’s article about elementary children’s negotiation of analytic frames in deliberative dialogue during input into a school governance decision, Bickmore argues for the value of such agentic, citizenship-relevant learning opportunities in public schools. She points to their unfortunate infrequency (to the detriment of socially just democracy) in economically and racially marginalized communities. The concept of analytic frames is compared with the notion of interests—desires, needs, concerns, and ethical principles—underlying each party’s proposals in integrative negotiated conflict resolution theory. Questions are raised about the roles played by cultural context and status inequalities within dialogue groups. Bickmore concludes that both Hauver’s research methodology and her pedagogy of listening intently to children show enormous potential for enhancing transformative democratic education.
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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 | 0.021 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.022 | 0.045 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.020 | 0.036 |
| 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".