A Study of Urban Rankings as it Pertains to Facets of Environmental Sustainability
Bibliographic record
Abstract
A concern has arisen about the numerous rankings of sustainable, green and smart cities in various publications. Different publications are producing vastly different results. If a consistent and legitimate set of measures were being adopted by various researchers, there would be commonality among the rankings. However, this does not seem to be the situation. As such, this thesis explores the credibility of the published rankings of cities as to whether they are sustainable, green, or smart. To help meet this goal, this study explores how the rankings vary, what criteria are used, and how such rankings can be improved. In addition, a survey was conducted of experts in the field to help better construct ways of ranking cities, based on them being sustainable, green or smart. The findings show that the majority of the rankings of sustainable, green and smart cities are diverse and employ questionable techniques. Sources that publish such rankings seemingly compile random lists of cities with no research having taken place. The survey helped establish additional criteria by which to rank cities so as to create more consistent results.
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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.019 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".