Real- Time Review Terms of Reference 1. Background
Bibliographic record
Abstract
epicentre 16 km from the capital Port-au-Prince. This was the most powerful earthquake to hit Haiti in more than 200 years. It is estimated to have killed 217,749 and injured 30o,5721. The Revised UN Flash Appeal says that 3M people have been affected, of whom approximately 1.9 million have lost their homes and over 511,000 have left the affected cities. The most affected cities are Port-au-Prince, Carrefour, Léogane and Jacmel. Two month after the earthquake, major issues of concern remain in the areas of sanitation, vulnerability of the camps to flooding, overcrowding, spread of disease, impact of displacement on host communities and shortages of shelter materials. The scale of the disaster continues to be overwhelming for the local Government and the international community as a whole, who are looking simultaneously at the immediate crisis, impending hurricane season, and the early recovery and reconstruction phases. The Humanitarian Coalition is a coalition of four Canadian nongovernmental agencies (Care Canada, Oxfam Canada, Oxfam-Québec and Save the Children Canada) with decades of experience in humanitarian assistance, aid and development who undertake joint emergency appeals and action. In Haiti, the Humanitarian Coalition has more than 1100 aid personnel on the ground responding to the crisis and has collectively raised 13 million $CAD for the response to the Haiti Earthquake. The Humanitarian Coalition is working on developing and testing an
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 | 0.005 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.597 | 0.563 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".