Emerging trends and research frontiers in spatial multicriteria analysis
Bibliographic record
Abstract
A majority of research on Spatial Multicriteria Analysis (SMCA) has been spatially implicit. Typically, SMCA uses conventional (aspatial) multicriteria methods for analysing and solving spatial problems. This paper examines emerging trends and research frontiers related to the paradigm shift from spatially implicit to spatially explicit multicriteria analysis. The emerging trend in SMCA has been spatially explicit conceptualizations of multicriteria problems focused on multicriteria analysis with geographically varying outcomes and local multicriteria analysis. The research frontiers align with conceptual and structural elements of SMCA and pertain to, among others, theoretical frameworks, problem structuring, model parameter derivation, decision problem contextualization, scale representation, treatment of uncertainties, and the very meaning of decision support. The paper also identifies research directions and challenges associated with developing spatially explicit multicriteria methods and integrating concepts and approaches from two distinct fields: GIS and multicriteria analysis.
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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 | 0.040 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.013 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".