Applying voting theory in participatory decision support for sustainable timber harvesting
Bibliographic record
Abstract
Several multi-criteria decision support methods have been introduced to sustainable management of natural resources, but different methods suit different planning situations. One way to support decision-making is to apply voting theory. In this study, a multi-criteria decision-support method based on voting theory, called multicriteria approval (MA), is applied to wood supply chain management in a forest area owned by the state of Finland. The area is called Leikko and is located in the rural municipality of Pieksämäki. MA seems to have some promising features in relation to participatory decision support. The most essential advantages are its ease and comprehensibility. MA is also able to deal with ordinal and imprecise information. Since the method does not demand much preference information from interest groups, the inquiries may be conducted using the Internet. In the case study, nine timber-harvesting alternatives were devised for the forest area. The study involved seven interest groups, whose representatives defined seven criteria by which the alternatives were compared. The purpose was to find a consensus or compromise solution for a practical harvesting schedule. Two different versions of MA were tested and compared from the participatory decision-support aspect. Usability and ease of method, the comprehensibility of the inquiries, and the congruence of the results were examined.
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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 | 0.062 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".