Investigating the key indicators for evaluating post‐disaster shelter
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
This study sought to identify the primary indicators for evaluating shelter assistance following natural disasters and then to develop a shelter evaluation instrument based on these indicators. Electronic databases and the 'grey' literature were scoured for publications with a relation to post-disaster shelter assistance. Indicators for evaluating such assistance were extracted from these publications. In total, 1,525 indicators were extracted from 181 publications. A preliminary evaluation instrument was designed from these 1,525 indicators. Shelter experts checked the instrument for face and content validity, and it was revised subsequently based on their input. The revised instrument comprises a version for use by shelter agencies (48 questions that assess 23 indicators) and a version for use by beneficiaries (52 questions that assess 22 indicators). The instrument can serve as a standardised tool to enable groups to gauge whether or not the shelter assistance that they supply meets the needs of disaster-affected populations.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it