Online Dispute Resolution (ODR) within Developing Nations: A Qualitative Evaluation of Transfer and Impact
Bibliographic record
Abstract
The field of online dispute resolution (ODR) is developing both as practice and a profession. Evidence of this includes a growing community of scholars and practitioners. A Canadian International Development Agency (CIDA) grant permitted 16 practitioners from developing countries to attend the 2008 ODR Forum in Victoria, British Columbia. In the year following the Forum, an evaluation was conducted to identify changes among these practitioners’ behaviors, knowledge, skills, abilities and credibility. Results indicate that ODR practitioners in developing countries are engaged in a wide range of activities, many of which are technologically and logistically complex. These practitioners also face a number of political and infrastructural challenges that are not as commonly experienced by those from developed nations. Taken together, these realities have implications both for the nature of ODR’s proliferation as a legitimate practice, as well as for the provision of education and training concerning its underpinnings.
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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 | 0.100 | 0.095 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".