Haiti Four Years Later: Towards a Lasting Legacy for Haitian Children
Bibliographic record
Abstract
January 12, 2010, was a day of profound tragedy for Haiti. Four years after Haiti's epic earthquake, the numbers are still hard to accept. Over 230,000 people were killed in a matter of moments and 1.5 million others were displaced. More than 70,000 homes, businesses and public buildings were destroyed. The national government was crippled; the dead included 25 percent of all civil servants. Nearly 5,000 schools were damaged or destroyed as the ground convulsed beneath the capital of Port-au-Prince and the surrounding countryside. A fragile government, poor infrastructure and insecurity exponentially compounded the earthquake's impact, and left the population vulnerable to the cholera epidemic that affected over 630,000 people from October 2010, as well as hurricanes and tropical storms that caused flooding and wreaked havoc in 2012 and 2013. An end is in sight. Over 89% of the displaced population has left the camps; the incidence of cholera has been halved since the outbreak in 2010; severe food insecurity has been brought down from 1.5 million affected people in early 2013 to 600,000 by October 2013. Such progress was made possible by the power of your support, combined with our work and the incredible efforts of the Haitian people themselves. Now is the time to capitalize on this progress to achieve real lasting change. Now is the time to impact the lives of Haiti's most vulnerable. Now is the time to move together towards a brighter future for Haiti's children. These children still have critical unmet needs and acute vulnerabilities, requiring proven life and livelihood-saving interventions.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".