How can Canada help Haiti without making a bad situation worse?
Bibliographic record
Abstract
The situation in Haiti, especially in and around the capital Port-au-Prince, has deteriorated in recent months. In some areas gang violence and poverty is rampant, while food, water and fuel have become scarce. While many point to the assassination of former-president Jovenel Moïse as the catalyst for the current crisis, our guest today argues that the roots of the current tumult stretch back much further, and that past Canadian foreign policy decisions have contributed heavily.Now, there are calls for foreign military intervention to stabilize the situation, and suggestions that Canada should play a leading role in that effort.So what exactly is happening in Haiti? What do the Haitian people need to improve their situation? And if military intervention is not the answer, then what should Canada do to support Haiti in its struggle for peace, prosperity and justice?Guest: Jean Saint-Vil (Jafrikayiti), radio host and member of Solidarite Quebec-Haiti.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.060 | 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 teacher head, 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".