Sustainable territorial development index for assessment of metropolitan regions
Bibliographic record
Abstract
The rapid expansion of urban areas worldwide has sparked discussions about their environmental impacts and the consequences for residents' lives. In this context, this article introduces a method for evaluating sustainable territorial development (SustTD) in metropolitan regions, using the Curitiba Metropolitan Region (CMR) as a case study. The evaluation method is based on the concept of SustTD, which emphasizes territorial identities, integration of systems, policies, and activities, shared management, and common resource use. A set of indicators was developed considering natural, social, and built capital as the dimensions of sustainable development. The findings led to the creation of the Sustainable Territorial Development Index (IDTS3). Indicator values were normalized between 0 and 1, and exploratory analysis conducted for possible groupings of municipalities using the Ward method for hierarchic grouping using the Euclidian distance as a measure of dissimilarity. Contrary to the broadly advertised integration of the CMR, the results revealed an IDTS3 of 0.55, and only two municipalities achieved values higher than this average. This was confirmed by the groupings which pointed to high inequality among the CMR municipalities, with the capital, Curitiba, achieving a much higher IDTS3than the other municipalities, and showing that the CMR is a fragmented region with significant development disparities among its members.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".