Dealing in Black and White: The Diefenbaker Government and the Cold War in South Asia 1957–1963
Bibliographic record
Abstract
Abstract: This article examines how religious and cultural stereotypes of India and Pakistan affected the perceptions and attitudes of Canadian policy-makers and the Diefenbaker government from 1957 to 1963. Canadian policy-makers initially believed that India's elites shared numerous commonalities with the West that were rooted in a shared language and a similar judicial and democratic parliamentary system. Through these ‘commonalities’ officials believed that India would align with the West as the Cold War intensified. Such assumptions proved to be ill-considered. The Indian government practised a doctrine of non-alignment in the 1950s, and senior Canadian officials regarded this as hypocritical and even anti-Western. By contrast, Pakistan aligned with the West and raised its anti-communist rhetoric. A growing pool of Canadian officials looked to Islam, Hinduism, and cultural stereotypes to explain why Pakistan differed from India and perceived the threat of communism in the same way as the West. These officials also re-evaluated the importance Ottawa placed on ties with New Delhi. The election of Prime Minister John Diefenbaker in June 1957 hastened this trend. Diefenbaker viewed communism in binary terms and concluded that, unlike India, Pakistan was on the ‘right side’ in the Cold War. The Diefenbaker era saw Ottawa relegate India to the periphery of its foreign policy interests and bilateral relations with Pakistan at their apogee.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".