Making injustice explicit: The externalist approach to reciprocal elucidation
Bibliographic record
Abstract
Genuine dialogue seems to require sincere commitment to reciprocal questioning. This view is reinforced by the idea of reciprocal elucidation, which tempts us to see it as binary: both engage, and understanding flows; one refuses, and nothing happens. This makes it seem that real dialogue depends on the other's ethical choice. I challenge that intuition. Instead, I propose that by adopting an externalist view, we can see that dialogue reveals the character of its participants, regardless of intent. Thus, rightly understood, reciprocal elucidation isn’t confined to ideal cooperation—it can expose injustice and asymmetry, even when one party manipulates or withholds meaning. Its power lies beyond mutual consent.
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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 | 0.024 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.079 |
| Scholarly communication | 0.015 | 0.022 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".