AndrewExamining the Claims of Environmental ADR Examining the Claims of Environmental ADR Evidence from Waste Management Conflicts in Ontario and Massachusetts
Bibliographic record
Abstract
During the past decade, alternative dispute resolution (ADR) techniques haveincreasingly been applied to environmental conflicts. These methods have been substituted for more conventional and adversarial conflict resolution processes, most commonly quasi-judicial public board hearings in Canada and litigation in the United States. The three principal ADR methods are negotiation, facilitation, and mediation. Previous writing on environmental conflict resolution is overwhelmingly positive about the benefits of ADR for settling these types of disputes. Dominant in the litera-ture are claims that it is very successful in reaching agreements and likely to do so faster and at less expense than conventional conflict resolution channels. Many authors boast of the high reported rates of participant satisfaction with ADR processes and their outcomes. Some attribute the success of facilitation and mediation to the benefits accruing from the intervention of a neutral party. Still others expound on the virtues of a process that is open to all affected stakeholders. In fact, so many authors have extolled the merits of ADR that their claims have achieved near-mythical status. Despite many similar claims of the virtues of ADR in environmental disputes, there have been very few examples of their being empirically tested, noted Wiedemann and
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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 | 0.026 | 0.123 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.017 | 0.012 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".