A method for examining the spatial dimension of multi-criteria weight sensitivity
Bibliographic record
Abstract
There is growing interest in extending GIS to support pluralistic decision-making processes where the perspectives and objectives of different stakeholders must be represented and, if possible, distilled into strategies that satisfy all decision participants. Augmenting GIS capabilities with multi-criteria decision-making (MCDM) methods allows the relative attractiveness of different alternatives (e.g. sites, land-use plans, etc.) to be evaluated in light of subjectively weighted decision criteria. This paper presents a generic methodology for investigating the spatial dimension of multi-criteria weight sensitivity. The methodology is particularly well suited to the spatial domain, as it provides insight into both the robustness of individual stakeholder's evaluations as well as the geographic dimension of weight sensitivity. The methodology is illustrated using a study in which a small group of individuals representing different interests evaluated sites for new tourism development on the island of Grand Cayman, BWI. The results demonstrate how the proposed approach can aid users' understanding of a decision issue and potentially increase confidence in evaluation outputs by providing users with mechanisms to define non-statistical confidence intervals for weights and to visualize weight sensitivity cartographically. The paper concludes by discussing the broader value of this approach in other GIS-MCDM contexts and outlines areas for further research.
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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 | 0.015 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".