Africa’s Resource Curse: Canadian Mining Companies and Private Security Actors, who is the Authority?
Bibliographic record
Abstract
The New Source of African Security?Africa's Resource Curse: Canadian Mining Companies and Private Security Actors, who is the Authority?The map above displays the location, type, and number of Canadian mining operations in Africa.Africa has long been plagued by what has become known as the 'Resource Curse'.This explains a situation in which foreign investment in the country has resulted in further state and political corruption, environmental degradation, and instability, without any real benefit trickling down to the grass roots level of local communities.As shown by the map above, Canada has a huge stake in African mining, with 17% of Canadian mining assets abroad being located in Africa.More importantly, Canada is the second largest mining investment in Africa, falling only behind South Africa itself.Due to the size and number of Canadian companies operating in Africa, as well as their influence on state stability, investigation of this field has grown more than pertinent.The research I conducted focused on what Canadian companies were operating in Africa, how big their operations were, and what type of relationship these companies had with both local government and local populations.While some companies have had a positive presence on the continent, working in tandem with communities to increase overall human security and build mutually prosperous relationships, there is a great deal of evidence to suggest in most cases, Canadian mining companies are not welcome in local communities due to the environmental damage, land redistribution, and societal harm they cause.The majority of Canadian resource companies operating in Africa have relationships that are tenuous at best with local populations.Most companies experienced several workers strikes, many violent (usually the result of poor working conditions and low wages), including companies such as First Quantum Limited, Lundin Mining Corporation, Eastern Platinum Limited, Great Basin Gold Limited, Golden Star Resources Ltd., Uranium One, SEMAFO, and Rockwell Diamonds Inc.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.028 | 0.018 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".