Web-Enabled Spatial Decision Analysis Using Ordered Weighted Averaging (OWA)
Bibliographic record
Abstract
This paper presents a spatial decision support tool that implements the Ordered Weighted Averaging (OWA) method. OWA is a family of multicriteria evaluation operators characterised by two sets of weights: criterion importance weights and order weights. We propose a highly interactive way of choosing, modifying, and fine-tuning the decision strategy defined by the order weights. This exploratory approach to OWA is supported by a graphical representation of the operator's behaviour in terms of decision risk and tradeoff/dispersion between criteria. Our prototype implementation is based on the CommonGIS software, and thus, Web-enabled and working with vector data. We successfully demonstrate online, exploratory support of spatial decision strategies using a data set of skiing resorts in Wallis, Switzerland.<div><br></div><div>This is a post-peer-review, pre-copyedit version of an article published in Journal of Geographical Systems. The final authenticated version is available online at: http://dx.doi.org/10.1007/s101090300095 <br></div>
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".