Mobile Online Dispute Resolution Tools’ Potential Applications for Government Offices
Bibliographic record
Abstract
Mobile Online Dispute Resolution Tools’ Potential Applications for Government Offices Online communication practices have become intrinsic to government work environments. Understanding the impact of these practices, whether they be general computer mediated communication (CMC) or specifically online dispute resolution (ODR) processes, is an essential step in supporting respectful and healthy work environments. ODR literature focuses almost exclusively on e-commerce, leaving large gaps in the body of knowledge as ODR applications diversify. Available ODR tools, which simply transpose traditional alternative dispute resolution (ADR) processes online through the use of office videoconferencing systems, are not mobile and do not utilize the full capabilities of the existing technology. This article explores the potential impacts mobile ODR (MODR) tools could have on the dispute interventions and prevention initiatives in government office settings. The study used an exploratory model to establish an understanding of the experiences and needs of Canadian and Australian government employees. Findings demonstrate an interest in the introduction of education-oriented MODR tools as supplementary support with the purposes of knowledge retention and further skill development following dispute prevention training. Findings suggest that workplace attitudes towards online communication and ODR have a significant impact on the extent to which individuals successfully develop and maintain relationships either fully or partially through the use of CMC.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.003 |
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".