Bibliographic record
Abstract
Narrator: Retired Lieutenant Commander Paul Seguna, 1957- Interviewer: Interviewed by Matthew Bacon Interview Date and Location: 03 March 2025, Victoria, BC. Synopsis: Retired Lieutenant Commander Paul Seguna discusses his experiences in the Canadian military, with a particular focus on his time at NATO Maritime HQ in Northwood, London. The first half of the interview delves into Seguna’s background information, Cold War experiences, and his time with the Canadian military as it transitioned into a post-Cold War era. The second half of the interview explores Seguna’s experiences in NATO operations and his time in NATO’s maritime HQ. Specifically, Seguna deployed overseas to Rwanda, Kosovo, and Bosnia and Herzegovina, the latter two under the auspices of NATO-led forces. The interview goes on to explore his time at NATO’s maritime HQ, wherein he expressed a renewed interest among NATO member states in the Atlantic. Furthermore, during his time there, Seguna explored his experiences regarding states participating in the Partnership for Peace program. 00:00-01:00—Introduction. 01:00-15:00—Background Information. 15:00-20:40—Cold War Service. 24:40-32:30—Cold War to Post-Cold War Experience. 32:30-48:20—NATO Operation Experience. 48:20-01:14:49—NATO Maritime Command HQ Northwood (London, UK). 01:14:49-01:23:23—NATO Missions V.S. HQ Experiences. 01:23:23-01:24:57—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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.038 | 0.012 |
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".