Bibliographic record
Abstract
It is the aim of this article to propose a novel system of dispute resolution for disputes which turn on interpretations of complex but uncertain scientific evidence. Part II identifies a specific subset of legal disputes that can only be resolved through policy judgments from ambiguous scientific data. Recognizing the underlying commonalities of these science-policy disputes offers an opportunity to craft a single dispute resolution mechanism which may be utilized for a wide variety of disputes. Part III outlines the benefits of using a mediation-based dispute settlement mechanism, as opposed to the traditional adversary-style litigation system, for these specific types of disputes. Part IV proposes a model mediation system for disputes turning on policy-based interpretations of complex scientific information. Part V concludes by applying the model to a fictional products liability dispute which involves conflicting scientific determinations from technically complex data.
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 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 | 0.055 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.020 | 0.024 |
| Open science | 0.008 | 0.020 |
| Research integrity | 0.015 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 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".