Bibliographic record
Abstract
Martin Buber provides an ethical understanding of dialogical resistance. But does this notion take sufficiently into account the oppositional force of resistance and the shifting realities of monologic relations? How are we to understand the terms dialogue and resistance? What impact will the ethics of dialogical resistance have on evaluation practices in public education? To address these questions, each term of this dyadic relationship must be defined. First I will differentiate dialogue from conversation, argument and discussion. Secondly it must be shown that my view of ethical resistance cannot be synonymous with criticism, disagreement or dissent per se, though undoubtedly certain connections do exist in practice. Then it will be appropriate to delve into a linguistic analysis of the substantive terms of dialogue and resistance as separate notions before using them together as intersecting concepts. Once I have delineated dialogical resistance as a dyadic tension, I will highlight Martin Buber's passion for human worth – the motivation for respect- as the necessary condition for the ethical success of dialogical resistance. The balance of this paper will take a look at the psychological roots of dialogical resistance, the complexity of practising dialogical resistance, and asymmetrical relations in the classroom.
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.035 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 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".