Key Considerations: COVID-19 in Informal Urban Settlements (March 2020)
Bibliographic record
Abstract
This brief sets out key considerations for protecting informal urban settlements from the spread and impacts of COVID-19. There is heightened concern about these settings because of the combination of population density and limited infrastructure. This briefing discusses what is known about vulnerabilities and how to support local action. It can be viewed in conjunction with the Science in Humanitarian Action Platform (SSHAP) briefings on quarantine and social media. This brief was developed for SSHAP by the Institute of Development Studies (IDS) with contributions from the Global Challenges Research Fund (GCRF) Accountability for Informal Urban Equity Hub (ARISE), the Asian Coalition for Housing Rights, the International Institute for Environment and Development (IIED), University College London (UCL) and the UCL/Development Planning Unit (DPU), University of Birmingham, University of Lincoln, University of Manchester, University of Warwick, WEIGO and York University (Canada). It was reviewed by colleagues at Anthrologica, IIED, University of Manchester, UCL/DPU, IFRC and UN-Habitat. The brief is the responsibility of the SSHAP.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.009 | 0.004 |
| Insufficient payload (model declined to judge) | 0.086 | 0.027 |
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".