Bibliographic record
Abstract
Abstract What does Asia's rise mean for Canada? This introductory essay explores some of the economic, political, and military dimensions of Canada's relationship with China and India in particular, and how Canadian foreign policy might be developed to deal with a new reality where the world's economic and political centre of gravity is in the Pacific Ocean, rather than the north Atlantic. It also argues that using an informal “grand strategy” heuristic is a potentially useful way of assessing how to develop foreign policy in a complex and dynamic environment. Keywords: CanadaChinaAsiastrategyforeign policy Acknowledgments The articles in this special issue were first presented at a two-day workshop held at Carleton University. After a rigorous selection process, 14 papers were presented. After a round of excellent discussant comments during the workshop, the seven articles found in this special issue went through an extensive peer review and emerge as you see them here. Without the generous gifts of time, energy and insight provided by our discussants and peer reviewers, the articles (and this special issue) would not have happened. To that end, we would personally thank the paper discussants at our conference: Jean Daudelin, Yiagadeesen (Teddy) Samy, Dane Rowlands, David Carment, David Malone, David Long, Yanling Wang and Kyle Christensen. We would also thank our anonymous peer reviewers. Finally, the special issue was ultimately made possible due to the generous financial support of the Social Sciences and Humanities Research Council of Canada, the Security and Defence Forum Special Project fund and the Centre for Security and Defence Studies. Carleton University and the Norman Paterson School of International Affairs provided invaluable institutional support. Notes For example, Angus Maddison's historical GDP database estimates China's GDP to be approximately 90 per cent of America's in 2008, using 1990 Geary-Khamis dollars as the unit of measurement. These numbers were estimated using the Stockholm International Peace Research Institute's Military Expenditure database, 1988–2011. All trade statistics estimated using the International Monetary Fund Direction of Trade Statistics database. All foreign direct investment statistics estimated using Department of Foreign Affairs and International Trade Canada Foreign Direct Investment database. Statistic calculated using Alberta Energy's leased oilsands area data and statement from Nexen that they have interests or claims on approximately 300,000 acres in Athabasca. Additional informationNotes on contributorsSimon Palamar Simon Palamar is a doctoral candidate at Carleton University's Norman Paterson School of International Affairs and works at the Centre for International Governance Innovation. Eric Jardine Eric Jardine is a PhD Candidate in the Norman Paterson School of International Affairs, Carleton University. He is a recipient of a 2012–2013 SSHRC Doctoral Fellowship. He has published extensively on insurgency and counterinsurgency in a number of peer reviewed journals, including Journal of Strategic Studies, Small Wars & Insurgencies, Civil Wars, Defence Studies, Parameters, Military Review and The Journal of Military and Strategic Studies, among others. Email: ehl.jardine@gmail.com
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".