Aggregation of preferences in participatory forest planning with multiple criteria: an application to the urban forest in Lycksele, Sweden
Bibliographic record
Abstract
A promising approach for participatory forest management planning is the combination of multiple-criteria decision-making and group decision making. A crucial part of the participatory multiple-criteria decision-making process is the aggregation of individual stakeholder preferences into a collective preference. In this study, an approach based on the determination of cardinal compromise consensus was applied to a real case of participatory forest planning. Consensus matrices for four different social groups were established from stakeholder preferences in the form of pairwise comparisons of different sets of criteria. Criteria weights were obtained for each social group and used to determine rankings of 12 forest management plans. The rankings of the social groups were aggregated to determine consensus solutions for the choice of the best forest management plan from a collective perspective. In the procedure, control parameters and a distance metric were employed to find solutions that balance the points of view of the majority and the minority. This approach makes it possible to aggregate preferences of different stakeholders and produces a range of different solutions. Furthermore, certain values of the control parameters and the distance metric generate solutions that are promising to present in a participatory situation where stakeholders have very differing preferences.
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.
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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".