Analysing and Anticipating Conflict Using a Values-Centred Online Survey
Bibliographic record
Abstract
The authors present an approach to conceptualising and predicting environmental conflicts in which conflicts are analysed as a continuum of disagreement over values and options. They also operationalise this approach using an online values-centred survey tool, the ‘public-to-public decision support system’ (P2P-DSS). The authors put values and conflict in environmental management into perspective. Next, they review how values are defined in scholarship and operationalised for decision support. The relevance of values research to con-flict management is presented. With reference to a real-world aggregate-mining conflict, the authors demonstrate how P2P-DSS can be used to collect data and categorise conflicts to enhance environmental management decision-making. The authors argue that P2P-DSS has potential to support values-sensitive thinking for environmental conflict management. They then set out research priorities to investigate the theoretical and practical implications of this approach. This work contributes to advancing values research in environmental conflict management and expanding values-based decision-making.
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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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".