Dynamic equity in urban amenities distribution: An accessibility-driven assessment
Bibliographic record
Abstract
The continual challenges exist in attaining an equitable allocation of urban amenities. In order to render this objective attainable as well as practical in real-world scenarios, it is imperative to transition from a static conceptualization of equity to a dynamic notion of equity. To assess dynamic equity in the context of Dhaka, we collected data for two distinct time periods, 2005 and 2018, and calculated integrated accessibility indices using enhanced two-step floating catchment area method. We then responded to these questions: a) Has there been an increase in the level of accessibility? b) Is the changed accessibility being shared more equally or unequally? c) Do members of underprivileged groups enjoy greater access than members of privileged groups? d) Is the underprivileged population enjoying progressively increasing access over time? The results indicate the distribution of accessibility exhibits a pattern, wherein individuals belonging to the underprivileged group experience lower accessibility benefits, while individuals in the privileged group enjoy better accessibility. The sole reason for optimism regarding the distribution pattern lies in the narrowing of the accessibility gap between the privileged and underprivileged. Moreover, the accessibility distribution becomes more equal within the underprivileged group, while becoming more unequal within the privileged group.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".