Bibliographic record
Abstract
This paper examines the dynamics of poverty and vulnerability in Haiti using various data sets. As living conditions survey data are not comparable in this country, we first propose to use the three rounds of the Demographic Health Survey (DHS) available before the earthquake. Decomposing household assets changes into age and cohort effects, we use repeated cross-section data to identify and estimate the variance of shocks on assets and to simulate the probability of being poor in the future. Poverty and vulnerability profiles are drawn from these estimates. Second, we decompose vulnerability to poverty into various sources using a unique survey conducted in 2007 in rural areas. Using two-level modelling of consumption/income, we assess the impact of both observable and unobservable idiosyncratic and covariate shocks on households'economic well-being. Empirical findings show that idiosyncratic shocks, in particular health-related shocks, have larger impact on vulnerability to poverty than covariate shocks. Third, asset-wealth is characterized for households after the 2010 earthquake based on a survey designed to provide a rapid assessment of food insecurity in Haiti after the quake. Whereas it is not possible to confirm the existence of poverty trap, it seems that those households who have lost the most due to the earthquake succeeded in recovering more rapidly from the shock, regardless of the effects of assistance, and probably more in line with coping strategies that are specific to households.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".