Bibliographic record
Abstract
Narrator: Boutilier, James., 1939- Interviewer: Interviewed by Eric Springgay–Daubeny Interview Date and Location: March 19, 2025, Zoom Synopsis: Interview begins by exploring the Boutilier’s personal background, delving into his experiences joining the Royal Canadian Navy, completing a PHD, and working in the South Pacific. This is followed by an exploration of experiences teaching at Royal Roads Military College and advocating for the founding of Royal Roads university, as well as the process through which he became involved with Maritime Forces Pacific (MARPAC) as a Special Policy Advisor. Boutilier discusses his engagement with diplomats and academics from the Asia Pacific region, his day-to-day activities as an advisor, and how he felt about Canada’s image in the Pacific during this period. Boutilier also explores his engagement with changing technology, the Navy’s role in determining Asia-Pacific policy, and his perspective on policy shifts between 1995 and 2020. 0:00:00 - 0:01:06 - Introduction 0:01:06 - 0:06:19 - Family background, upbringing in Bedford, Nova Scotia, broad-scale early career overview. 0:06:19 - 0:15:48 - Joining the Royal Canadian Navy, crossing the Atlantic Ocean for the first time, teaching navigation, Flying Tiger Line Flight 923 crash response. 0:15:48 - 0:19:11 - Experiences with the Royal Navy Reserves, North Sea minesweeping, 0:19:11 - 0:29:17 - First exposure to South Pacific history, Getting a PHD at the University of London. 0:29:17 - 0:34:36 - Working at the University of the South Pacific in Fiji, returning to Canada to work at Royal Roads Military College, relevance of Pacific experience when working at MARPAC. 0:34:36 - 0:42:53 - The decline and closure of Royal Roads Military College, creating Royal Roads University. 0:42:53 - 0:49:49 - Being recruited by MARPAC in 1995, early experiences, contrasts between different leaderships. 0:49:49 – 0:59:43 - Responsibilities at MARPAC, experiencing the “golden age of track-2 diplomacy,” attending academic meetings in Asia, disillusionment with leadership on Asia-Pacific issues. 0:59:43 - 1:08:55 - Canada’s Atlantic bias, impressions of Canada’s perception in the Pacific 1:08:55 - 1:15:20 - Engagement between Canada and China, naval diplomacy. 1:15:20 - 1:20:49 - Impact of budget concerns, finding money for travel expenses, collaborating with the US Navy in the Pacific, 1:20:49 - 1:26:20 - Incorporating technology at the personal level and strategic level, communicating technological change to MARPAC leaders. 1:26:20 - 1:32:29 - Opinions on the effects of government changes on policy, retrospective analysis of Canadian naval policy since 1995. 1:32:29 - 1:36:27 - MARPAC’s role in filling an Asia-Pacific policy vacuum, diplomatic contribution of the Navy. 1:36:27 - 1:40:55 - Trying to get other ministries engaged in Indo-Pacific relations, struggles to find funding for defense conferences. 1:40:55 - 1:51:54 - Frustrations about Arctic policy and AOPS program, feelings about contemporary policy making. 1:51:54 - 1:54:33 - Concluding remarks
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.034 | 0.008 |
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".