Exploring the Local Sustainability Approach Using Indicators
Bibliographic record
Abstract
The purpose of this research was to analyze the application of the Barometer of Sustainability (BS) as a tool for monitoring the sustainability process, using the case of the municipality of Ribeirão Preto, Brazil. The method adopted was based on the important seven stages for the BS application. The methods used were exploratory, descriptive, analytical and field research approaches, combining primary and secondary data. BS as an evaluation tool has proved useful in contributing to the understanding of social and natural phenomena, providing the monitoring of sustainability on a local scale. The findings indicated that the municipality had a greater concern with socioeconomic issues in relation to environmental issues. Based on BS, Ribeirão Preto was classified as intermediate level in relation to Sustainable Development, presenting better performance in the Human Subsystem. To solve the main methodological difficulties related with sustainability indicators to measure the sustainability dimensions on local level, and transpose these challenges is a continuous and emergency process. The integration of information from institutional bodies and sharing of data are paramount for public management at the municipal level to help develop and consolidate national databases. In this paper the authors demonstrated that is necessary to develop efficient methods of sustainability evaluation for local practice to develop policies and actions and add value in the decision-making process of local governments.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".