Creating a performance indicator for temporary housing
Bibliographic record
Abstract
Eighty percent of the cities in the world are located in areas that are affected at least once a year by natural disasters. As a natural disaster occurs, dislodged people need a place to stay. Between the timeframe of people living in congregate emergency shelters and going back to living in permanent housing, there is a long period where people need to live in temporary houses. Most research works have focused predominantly on categorizing temporary houses and have neglected to create an assessment tool for selecting the most optimal temporary housing option based on their performances. This research aims to: (i) investigate the relevant vital indicators affecting the post-disaster temporary house construction process; (ii) create a performance indicator (PI) table to help in creating more sustainable and resilient temporary houses; (iii) examine each of the existing temporary houses based on the factors derived from the PI table; and (iv) create a numerical scale from the evaluation table to compare options and measure their performance. This research would act as a guide for stakeholders to find the most appropriate option for the region based on the unique characteristics of the event and the available equipment and facilities.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".