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Record W2552313714

Africa’s Resource Curse: Canadian Mining Companies and Private Security Actors, who is the Authority?

2011· article· en· W2552313714 on OpenAlexaboutno aff
Sasha Langille-Rowe

Bibliographic record

VenueuO Research (University of Ottawa) · 2011
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsnot available
Fundersnot available
KeywordsResource curseCurseBusinessResource (disambiguation)Private securityNatural resourcePolitical sciencePublic administrationLawComputer scienceSociology
DOInot available

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.075
Threshold uncertainty score0.541

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0280.018
Scholarly communication0.0150.008
Open science0.0010.004
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.0170.001

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.

Opus teacher head0.055
GPT teacher head0.235
Teacher spread0.180 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

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Citations0
Published2011
Admission routes1
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