Dealing with conflict: Natural resources and dispute resolution
Bibliographic record
Abstract
Conflicts over natural resources are becoming more frequent due to increasing populations, the clash between different value systems, and the greater economic and environmental demands on finite resources. The dynamics of conflict are complex as a result of interacting factors related to the parties involved, the nature of the resource, and the stage of development of the conflict. Where people are denied access to resources or are continually marginalised from resource-planning processes, disputes may escalate to civil strife. While the underlying causes of conflict may be clear, there is an urgent need for practical methods to address and resolve conflict. Mechanisms are required to promote understanding and cooperation of an increasing number of stakeholders, especially if resources are to be sustained to support present and future generations. The International Model Forest Network (IMFN) programme is one example of a multi-stakeholder approach in conflict prevention and resolution at the landscape level of resource management. The 'model forest' is essentially an experiment in partnership building. The programme is briefly described. It started in Canada in 1991 in order to address the challenges of sustainable forest management while taking into consideration economic, environmental, social and cultural needs, and was expanded a year later (at the 1992 UNCED Earth Summit) to include model forest initiatives in Mexico and the Russian Far East. The USA has recently joined the network. (CAB Abstract)
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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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.021 | 0.016 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.034 | 0.007 |
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".