Bibliographic record
Abstract
Canada was among the first countries to participate in India’s nuclear power program. Not only was CIRUS the first research reactor to be built in India with outside help; it was also Canada’s first reactor export and marked a breakthrough for its nuclear industry. But the nuclear relationship foundered when India conducted a peaceful nuclear explosion in 1974. Differences over non-proliferation and Cold War considerations kept India and Canada apart until the turn of the millennium. Then a number of factors coalesced in the first decade of the 21st century to lead the two countries to reconsider their nuclear engagement and sign a nuclear cooperation agreement in 2010. What made this possible? What are the major contours of the agreement? What potential does it hold? And what issues still bedevil the nuclear relationship? This article provides a new perspective on the possibilities and challenges of the Indo-Canadian nuclear relationship and highlights the importance of the nuclear dimension in their larger bilateral engagement. While the paper acknowledges that the relationship was influenced by Cold War considerations, it steers clear of the issues of alignment and non-alignment and deals with the historical context of the relationship only to extrapolate the possibilities and challenges for the future.
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 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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.038 | 0.015 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".