Residential choice from a multiple criteria sustainable perspective
Bibliographic record
Abstract
Abstract Minimizing travel in the urban environment facilitates the development of sustainable cities. A key aspect is that there is a wide supply of amenities and facilities in the neighbourhoods: if most of the needs of families, goods and services can be covered from the sub-centers of the residential areas, it will be possible to reduce daily intra-urban mobility. The objective of this work is to propose a ranking multicriteria method that facilitates the choice of an ideal residential location in terms of neighbourhood characteristics, especially in the search of sustainable mobility for each family characteristics. One of the main problems in several Multiple Criteria Decision Making methods is the assignment of criteria weights in the aggregation process. The proposed methodology in this paper, Un-weighted TOPSIS (UW-TOPSIS) is able to overcome that problem. In this Multiple Criteria Decision Making (MCDM) method the relative proximity of each decision alternative to an ideal solution is minimized for the un-known weights of the criteria which are the variables in the corresponding mathematical programming program. Thus, a ranking based on the relative proximity of each alternative to an ideal alternative is obtained without the a priori establishment of the criteria weights. The use of subjective weights in real decision making contexts, where for instance a ranking of alternatives is required, is subject to important criticisms. This could be the case of the ranking of neighbourhoods based on their sustainability.
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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 | 0.003 | 0.003 |
| 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 | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".