A MULTIDIMENSIONAL AND PARTICIPATORY APPROACH FOR GREEN BUILDINGS ASSESSMENT
Bibliographic record
Abstract
The development of complex decision making processes has encouraged the involvement of different stakeholders in the evaluation procedures and tools. Multi-criteria evaluations are increasingly being used in deliberative evaluation process, addressing research experiences and applications towards this new challenge: to give a broader and stronger meaning and consistency to the outcomes of the decision making process. It means to open the decisional arena to different groups with different points of view and involve multiple weights in the multicriteria evaluation framework. Itís widely acknowledged that the need for evaluation tools aiding the complex decisions comes from the consciousness about uncertainty (Funtowicz and Ravetz 1994), that requires to focus more on the process than on the outcomes. According to these general assumptions, the paper gives a critical review of assessment methods and tools developed in the field of the performance assessment of buildingsí sustainability by the Green Building Challenge (GBC) process ñ launched by Natural Resources Canada in 1996 and managed by the International Initiative for a Sustainable Built Environment (iiSBE) in 2002 ñ in order to point out 1) their strenghts and weaknesses and 2) to understand the opportunities to adapt their evaluation framework to the principles of deliberative multicriteria evaluation (Proctor, Drechsler 2006). With reference to the outcomes of this analysis, the paper suggest to enforce the followings issues of the last version of Green Building evaluation tools, called Sustainable Building Tool (SBTool): the evaluation process, the choice of criteria and the weighting system. More in deep, the analysis highlights that weights assignment is the most critical stage, because it is based on preferences and priorities of some decision-makers only (experts, technicians and institutional ones), involved with the task of adapting the generic evaluation framework to the conditions of different context in various regions by tuning values and weights. To solve this criticism itís important involve other stakeholders in the weighting stage, using specific participatory rules, in order to make the weights assignment as transparent as possible and to strengthen the legitamicy of decisions taken (Munda 2004).
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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 | 0.015 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| 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".