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 <P, Φi, ◇Φ>, 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".