Beyond apartheid: moral identity, FIPAs, and NEPAD in Canada–South Africa relations
Bibliographic record
Abstract
Canada has more often than not portrayed a moral image in its relations with South Africa. However, many would agree that Canada has a mixed record in South Africa and that the pursuit of human rights and racial equality in Canada's foreign policy is accompanied by the pursuit of economic interests. Canada–South Africa relations received a major boost under the Chrétien government when the two countries forged closer ties and used multilateral institutions such as the Commonwealth to promote human rights, rule of law, and democracy particularly in African states. At the bilateral level, Canada signed a Foreign Investment Promotion and Protection Agreement with South Africa. Moreover, the launching of the New Partnership for Africa's Development in 2001 helped to further deepen Canada–South Africa economic relations. However, recent changes in the geographic focus of Canada's policy, especially the ‘look to the Americas policy’, raises the question as to where South Africa sits on the foreign policy agenda of Canada. This paper looks at Canada's economic relations with South Africa in the post-apartheid era through the lens of moral identity and asks, what are the impacts of the Harper government's ‘look to the Americas’ policy on Canada–South Africa economic relations? And does the promotion of human rights inform Canada's economic policy towards South Africa after apartheid?
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.049 | 0.027 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".