Päätösanalyysi vuorovaikutteisen suunnittelun tukena - Tapaustarkastelussa Koitereen säännöstelyn monitavoitteinen kehittäminen
Bibliographic record
Abstract
This thesis was done as a part of the development of the regulation of the Lake Koitere project. Starting point for this study was the positive experiences of applying decision analysis tools in regulation development projects in Finland and Canada. An approach, that combines the useful tools from both countries, was developed using these experiences as a foundation. The tools were applied to support the collaborative planning process and to improve the quality of the project. The goal of the study was to examine both the applicability of the decision analysis tools in collaborative planning and the applicability of the approach. The tools were applied in the work of the steering group of the development project. Value Focused Thinking was applied in a decision analysis work shop, in which the objectives of the different stakeholders were analysed and structured. REGAIM -model was applied in the personal decision analysis interviews. With REGAIM -model target regulations was created for the steering group members. The target regulations were used to create regulation alternatives and the impacts of the alternatives was discussed in the steering group. Next the conflicting objectives, that emerged, were compared in a trade-off analysis. At the end, through a few suggestions, a consensus solution between different stakeholders for the recommendations for the regulation practice was found. The decision analytic approach that was applied in the development of the regulation of the Lake Koitere was found useful. Both the feedback from the different tools and the answers from the enquiries pointed out, that the steering group of the project felt that the applied tools were useful for the whole process and for the outcome of the process. The approach helped in the reconciliation of the conflicting objectives of the different stakeholders. It also contributed to the consensus process where the confrontation between the stakeholders was remarkable in the beginning. Decision analysis tools can be applied in all kinds problem and they can be exploited in every planning process. Because of this, decision analysis tools and the approach that was developed in the development of the Lake Koitere can be put to use also in other environmental planning processes. Nevertheless, the tools that were applied in the approach should not be followed strictly. It is always important to think what tool responds to the needs of the particular planning process at hand.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".