Bibliographic record
Abstract
Narrator: Greenwood, Nigel. S., 1939- Interviewer: Interviewed by Eric Springgay–Daubeny Interview Date and Location: March 4, 2025, Zoom. Synopsis: Greenwood begins by exploring his family background and his early Royal Canadian Navy (RCN) experiences. This is followed by a discussion of his early career, highlighting his exchange with the US Navy and his increasing seniority in the 1990s and early 2000s. Greenwood discusses his experiences in the aftermath of the September 11th attacks, the RCN leadership’s struggle to balance federal priorities with funding shortfalls and declining enlistment, as well as his involvement with Federal Government policies such as the Arctic Offshore Patrol Ship program. Additionally, Greenwood provides extensive detail about experiences as an ice navigator after retirement, including his recollections of his time aboard the Chinese research vessel Xue long in 2017. 0:00:00 - 0:01:20 - Introduction 0:01:20 - 0:08:38 – Family background, upbringing in Powell River, childhood relationship to the Arctic, joining the Royal Canadian Navy, and Attending Royal Roads Military College, learning navigation. 0:08:38 - 0:16:26 – Early deployments and experience on exchange with Surface Warfare Development Group in US Navy. Contrasts between Arctic opportunities in Canada and US. 0:16:26 - 0:24:03 – Working with the US Navy in NATO operations in the Atlantic, Bilateral connections with the US Navy in the Pacific, RCN frustration with media response to 1985 Polar Sea traversal of the Northwest Passage. 0:24:03 - 0:29:17 – Career advancement in the 1990s, becoming a Captain. 0:29:17 - 0:40:05– Becoming Base Commander at CFB Halifax, impact of September 11th Attacks. 0:40:05 – 0:46:15 - Bureaucratic challenges, becoming Chief of Staff on the West Coast 0:46:15 – 0:52:55 - Arctic policy shifts in the mid-2000s, becoming Rear Admiral, engaging with Harper Government’s Arctic policy, internal perception of Arctic Offshore Patrol Ship program. 0:52:55 - 0:56:04 – Balancing Arctic policy with budget concerns and declining enlistment. 0:56:04 – 1:00:14 - Relationship with organizations such as the Canadian Coast Guard, Canadian Rangers. 1:00:14 - 1:09:44 – National Shipbuilding Procurement Strategy, 2009 Strategic Review, navigating financial questions. 1:09:44 - 1:16:23 – Retirement, becoming an ice navigator, 1:16:23 - 1:19:42 – Incorporating technological change, using GPS. 1:19:42 - 1:29:27 - Acting as an Ice Navigator for Chinese Research Vessel Xue Long in 2017. 1:29:27 – 1:36:43 - Views on Arctic misconceptions, changing Arctic environment, perspective on how climate change affected RCN policy. 1:36:43 –1:41:31 Closing 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.036 | 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".