Bibliographic record
Abstract
This paper considers the probative burdens of proposing action or policy options in deliberation dialogues. Do proposers bear a burden of proof? Building on pioneering work by Douglas Walton (2010), and following on a growing literature within computer science, the prevailing answer seems to be “No.” Instead, only recommenders—agents who put forward an option as the one to be taken—bear a burden of proof. Against this view, we contend that proposers have burdens of proof with respect to their proposals. Specifically, we argue that, while recommenders that Φ bear a burden of proof to show that □Φ (We should / ought to / must Φ), proposers that Φ have a burden of proof to show that ◇Φ (We may / can Φ). A burden of proposing may be defined as , which reads: Those who propose that we might Φ are obliged, if called upon, to show that Φ is possible in any of four ways which we call worldly, deontic, instrumental, and practical. So understood, burdens of proposing satisfy the standard formal definition of burden of proof.
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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 | 0.042 | 0.177 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.013 | 0.025 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.032 | 0.005 |
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".