Addressing Negative Externalities of Urban Development: Toward a More Sustainable Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The sheer size, growth, and complexity of cities worldwide are creating an ever-increasing burden of negative externalities on society and the environment. This systematic review aims to illuminate the broad range of negative urban development externalities and to analyze them in way that sharpens our ability to perceive, anticipate, and manage them. After finding that negative urban development externalities are more complex and diverse than has been previously articulated in the literature, the paper categorizes a representative sample by type (social, environmental, and economic) and identifies three modes of impact (visibility, emergence, and distribution) that make them extremely challenging to anticipate and mitigate. The most problematic negative externalities are social or environmental, with low visibility, cumulative patterns of emergence, and effects that extend beyond regulating jurisdictions. The analysis then draws on welfare economics to strengthen the case for the proactive management of these negative externalities and analyzes the competencies and capacities of local governments to strategically intervene in order to more effectively achieve sustainable development.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it