The Integration of Socio-Economic Indicators in the CASBEE-UD Evaluation System: A Case Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The use of tools to measure the degree of sustainability of cities is the approach that receives the most attention in developed countries. However, studies of evaluation tools at the neighborhood level reveal that there are many weaknesses in the most widely-used evaluation systems (LEED-ND, BREEAM Communities, CASBEE-UD). There are ambiguities and gaps in weighting and in scoring and in most cases, there is no mechanism for local adaptability and participation. The purpose of this study is to provide an overview of the current situation by highlighting the strengths and weaknesses of these evaluation tools in order to integrate social and economic aspects for the improvement of the CASBEE-UD (neighborhood level) evaluation tool. The selection of socio-economic aspects was made through the use of a multi criteria Analysis Hierarchical Process (AHP) and a Geographic Integration System (GIS). The results of this case study indicate that most evaluation tools need to be revised because most do not include socio-economic aspects. We have demonstrated that applying the CASBEE-UD assessment tool integrated with socio-economic aspects to four boroughs in the City of Montreal can measure success by addressing the objectives of sustainable development.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it