TURBULENCE IN MIRAMICHI BAY: THE BURNT CHURCH CONFLICT OVER NATIVE FISHING RIGHTS<sup>1</sup>
Bibliographic record
Abstract
Abstract: A systematic technique is proposed for assisting in the design and implementation of policy and addressing the need to minimize or resolve disputes that may arise in the enforcement of regulations. The Graph Model for Conflict Resolution is a methodology that facilitates the modeling and analysis of interactive multiple participant‐multiple objective decision problems. In the problems considered here, decision makers and policy planners engaged in capacity building typically have different viewpoints over appropriate ways of developing options and enforcing policy choices. Incompatible understandings of resource potentials and limits, and disparities in utilization of these resources, exasperate stakeholders and make the capacity building process counterproductive and even conducive to conflict. A systematic conflict resolution technique is invaluable to policy makers and practitioners in defusing confrontations and reaching out for consensus among participants. In support of current approaches to policy planning and regulation, the Graph Model provides accurate predictions and strategic insights into shortand long‐term opportunities in multiple participant‐multiple objective decision situations. A conflict among the government of Canada, the Mi'kmaq First Nation, and commercial fishermen over the sharing of a natural resource in New Brunswick, Canada, is used to illustrate the advantages of this technique in practical problems.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".