Maps and SDG11: A Complex but Possible Relationship
Bibliographic record
Abstract
Is it possible to visualize in an immediate and powerful way the indicators that measure the sustainability of cities?While the final answer is rather positive, the process of getting there is quite complex and dense with difficulties as illustrated in this paper.The 2030 Agenda and the Sustainable Development Goals (SDGs) constitute a fundamental apparatus for measuring and observing Countries based on specific analysis systems, but without an integration with spatial analysis tools as Geographic Information Systems (GIS).Although the potential of the latter in capturing efforts toward sustainable development is recognized by the United Nations (UN), their use in analyzing systems is limited considering data operability issues in terms of spatial and temporal accuracy and proprietary diffusion modes.This paper aims at bridging the gap between urban sustainability analysis and GIS tools, testing the integration of SDG11 indicators and GIS within the Italian context.The research here investigates the applicability and potential of such integration by considering the target 11.1 and 11.3 by focusing on a case study, the City of Turin (Piedmont, Italy).In operational terms, this paper implements an open access WebGIS database providing new spatialized sub-indicators based on the use of open data at the local urban scale.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".