The Impact of Multi-Criteria Decision Analysis Parameters on an Urban Deprivation Index
Bibliographic record
Abstract
Multi-criteria decision analysis (MCDA) is a family of decision support methods that allow analysts to structure a decision problem through the selection and evaluation of multiple and often conflicting criteria, using established techniques to standardize, weight, and combine these criteria. Through a case study of an area-based deprivation index for the city of Toronto’s 140 neighbourhoods, we examine the variability of MCDA results under different decision models. We use interactive cartographic visualization to explore the impact of criterion weighting and three decision rules: weighted linear combination, locally weighted linear combination, and ordered weighted averaging. The modelling of socioeconomic deprivation using these different decision rules and their parameters yielded different spatial patterns of deprivation for the same set of variables and weights. The results highlight the importance of examining multiple decision models before making policy recommendations. Keywords: analytic hierarchy process, area-based composite index, locally weighted linear combination, multi-criteria decision analysis, ordered weighted averaging, socio-economic deprivation
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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 | 0.014 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".