Solitary dissonance and collaborative consonance: trialogue as a reflective practice that resonates
Bibliographic record
Abstract
This paper represents a ‘trialogue’ between three individuals, all professors in a Faculty of Education, each at a different stage of the doctoral process: one, a recent graduate, one in the process of writing the final draft of her dissertation, and one just beginning. Evident throughout this article is the guiding assumption that learning is something that is experienced through reflective and collaborative practice – it is a journey rather than something that is acquired as a product or a destination. Three individual voices recount personal anecdotes; come to terms with dissonance through dialogue, valuation (valuing of), validation, and consonance. It is our hope that those who read this article will find within its pages their own dissonances, recognizing that a search for consonance brings with it important insights and understandings that will resonate for a lifetime.
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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 | 0.030 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.061 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".