The uneven terrain of dialogical encounters and the spatial politics of listening
Bibliographic record
Abstract
The contributions to this forum have highlighted how the limits to scholarly dialogue are multiple and have had serious consequences for the ways in which knowledges are produced and debated in the academy, the media, and wider society. In this rejoinder to the commentaries on our article, ‘The Possibilities and Limits to Dialogue’, we embrace the stance of affirmative critique in order to constructively engage with the important issues that our interlocutors raised. In particular, we consider questions of dialogical recognition, refusal, and the politics of listening as well as the need to strive not only to engage in dialogue but also to work toward changing the terms and terrain of dialogical engagement in order to produce a more equitable and just space of dialogical encounters in the academy.
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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 | 0.041 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.028 | 0.083 |
| Scholarly communication | 0.035 | 0.029 |
| Open science | 0.004 | 0.027 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 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".