Surveys for Urban Equity
Bibliographic record
Abstract
This dataset contains results and documentation from three cross-sectional urban household surveys done in Kathmandu (Nepal), Dhaka (Bangladesh) and Hanoi (Vietnam) in 2017 and 2018. The surveys primarily aimed to test the feasibility of using new urban household survey methods that try to better cover/capture informal/slum settlements using sampling frame data generated from random forest models that incorporate census data (which is often outdated and inaccurate) with multiple remotely-sensed covariates, such as urbanisation and infrastructure data. Additionally, the surveys also aimed to gather data on a range of topics including many that are not commonly collected in household surveys, particularly of urban areas: A) basic socio-demographic details of household members, B) household characteristics, assets, income and expenses, C) household migration and social capital, D) household member injury and injury related death, and, for one individual per household, E) migration, social capital and depression/mental health. See the "Readme - dataset file descriptions.docx” file for a description of all files and datasets available, plus additional relevant references.
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 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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".