Debt and Destruction: The Global Abuse of Haiti and Unbalancing the Myth of Benevolent Canada
Bibliographic record
Abstract
An integral responsibility of nation-states is to provide protection and the means for attaining a fulfilling life to those it governs. Given the fact that most current global powers were not founded with the needs of racialized peoples in mind, one is infuriated but not surprised, at the cyclical pattern of disregard and exploitation that people of colour in the Americas experience. Indigenous and Black communities in the Americas are not just disregarded by the state, but are actively targeted for exploitation and undermining. Analyzing Haiti’s post-colonial history and Canada’s domestic and international mining operations, I argue that nations in the Caribbean and Latin America have been extensively exploited economically by imperial powers, and their survival undermined by colonial legacies. Numerous countries in the region, to varying degrees, continue to experience the wrath of state-sponsored white supremacy and crippling debt that prevent authentic development. I advance the position that coerced debt and resource extraction have been weaponized against already ostracized communities by behemoth states that employ the myth of being a post-racial democracy. This paper also highlights a complex set of global relationships by linking extraction, state-corporate relations, and North-South divides, with a focus on Canadian mining in Latin America.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.016 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".