Dealing with conflict: Natural resources and dispute resolution
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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)
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it