Strategies to Support High-Growth Enterprises in Haiti
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Laine’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
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it