Complementary use of voting methods and interactive utility analysis in participatory strategic forest planning: experiences gained from western Finland
Bibliographic record
Abstract
This paper introduces the concept of complementary use of voting methods and interactive utility analysis (IUA) in strategic natural resource planning in western Finland as organized by Metsähallitus. The voting and IUA methods were used by three working groups, the aim in using these methods being to support the working groups in formulating their decision proposals for Metsähallitus. Ordinal preference information was collected by using the voting methods, whereas the IUA method defined the participants’ preferences by using the interval scale. In all the three working groups, four out of seven alternatives shared the first place in ranking when applying the voting methods, and one alternative was priorized over the other alternatives when using the IUA method. The stakeholders’ feedback indicated that the methods used were not too difficult to use and understand. The IUA method was especially efficient in promoting the participants’ learning process, which as a consequence made formulation of the decision proposal easier for them. It was also noticed that the standard versions of the voting methods do not necessarily fulfill the needs of decision support in strategic natural resource planning as such. Instead, complementary use with more profound methods (e.g., IUA) may be needed.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".