Online Dispute Resolution and Autism Spectrum Disorder: Levelling the Playing Field in Disputes Involving Autistic Parties
Bibliographic record
Abstract
Often, the overall success of an Alternative Dispute Resolution (ADR) process hinges on the ability of a neutral third party to establish a level playing field supported by a sense of equal bargaining power between disputants. Most forms of ADR, including traditional approaches to mediation and arbitration, are characterized by in-person interactions, where disputants and third parties communicate through a combination of verbal and nonverbal cues. Though many believe that this form of interaction is crucial for effective communication, it may result in significant disadvantages for autistic parties who face difficulties properly discerning the intentions or meaning of these cues.\nThis work examines the potential benefits of implementing Online Dispute Resolution (ODR) tools and platforms in dispute resolution processes involving autistic parties. It explores the inherent disadvantages presented by traditional forms of ADR and proposes an alternative approach geared toward the individual needs of parties and the accommodation of cognitive difference. Given the high potential for eased communication presented by computer and internet technologies for autistic disputants, this work posits that an ideal process would be one that effectively incorporates ODR tools and that provides a structured and stable environment for dispute resolution.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".