Bibliographic record
Abstract
Following World War II, Canada enjoyed an international standing unprecedented in the young nation’s history. Canada’s involvement in the foundation of the International Monetary Fund, and its contribution to the Marshall Plan and the engineering of the United Nations Emergency AQ5 Force, demanded new generations of specialized and skilled foreign policy-minded civil servants. With a donation from Senator Norman Paterson, the Norman Paterson School of International Affairs (NPSIA) was founded to address the issues of the post-war global environment. As a professional school of international affairs located in Canada’s capital, the School has emerged as an integral part of the nation’s international affairs community. To celebrate NPSIA’s 50th anniversary, the Canadian Foreign Policy Journal, in partnership with its policy outreach platform iAffairs, asked past and present Directors of the School about their views on its evolution throughout the years. The following is a brief compilation of transcripts from interviews with Christopher Maule, NPSIA’s Director from 1988 to 1993; Maureen Molot, Director from 1993 to 2002; Fen Hampson, Director from 2002 to 2012; and the present Director, Dane Rowlands, whose tenure began in 2012. The objective of the excerpts is to enrich our understanding of NPSIA’s past and future role as a professional school in the training of Canadian entrepreneurs, advisors, analysts, diplomats and policymakers.
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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.049 | 0.015 |
| Scholarly communication | 0.019 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.022 |
| Insufficient payload (model declined to judge) | 0.006 | 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".