Bibliographic record
Abstract
Although considered the poorest country in the western hemisphere for decades, worldwide attention was brought to Haiti following the devastating January 2010 earthquake. The calamitous disaster, with more than 200,000 killed and 1,500,000 displaced, highlighted underlying social, economic, and educational problems in the country. The outpouring of economic support for Haiti since the earthquake depicts the close connection between poverty and education. A statistical overview of Haiti provides some key indicators of this poverty-education connection: Less than 50% of the adult population is functionally literate, 80% of the population did not attend secondary school, and 78% live on $2 a day or less (World Bank, 2011). Yet in the midst of these challenges, innovative educational practices are presenting opportunities for Haiti to emerge from its impoverished state. Focusing on the intersection of poverty and education, the authors examined a number of innovative initiatives that used social capital as a tool for education reform–one involving a school near the capital of Port au Prince, another in the north region, and two projects focused on teacher and administrator training taking place across the country. The chapter concludes with an examination of common themes derived from the cases and considers how the social capital being developed through school reform initiatives can lead to a sustainable future for Haiti.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".