Bibliographic record
Abstract
For every person who is engaged with organizations working in Haiti, Catherine Lainé's narrative of the moments following the earthquake of January 12, 2010, will be a poignant reminder of the shock and the sense of uselessness we all felt as we watched the CNN reports live from Port-au-Prince.I was still watching in Dublin in the early hours of January 13, as we attempted to make contact with our colleagues and Haitian friends.AIDG's response to that emergency was mirrored by other similar organizations, as they cleverly applied their capabilities to suddenly revised priorities and did their best to respond to the new, more urgent, and more extreme needs of the people they served.By applying its skills and connections, AIDG found a way to participate in the immediate emergency response in a highly effective manner.Its support of Shelter2Home illustrates how a business development organization can use its expertise to help provide a response to a real and dire social need.Nature has set some high barriers to the development of Haiti with the risk of hurricanes, tsunamis, and earthquakes, but it is the man-made barriers highlighted in this case that deserve the most attention.While Haiti could be better prepared for and respond more effectively to the inevitable natural disasters, the Haitian leadership, supported by the international community, can and must take steps to remove many of the man-made barriers.One crucial short-term need identified by AIDG is for skilled masons and builders.The devastation in Port-au-Prince was a direct result of low building standards and workers' poor construction skills.As highlighted in this case, there is a real need to produce more skilled workers as the rebuilding process commences.Building back better will demand stronger construction skills and higher building standards.The case also highlights the gap in support for small and mid-size enterprises (SMEs), a vital sector of Haiti's future economic success.Many of the current
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".