Alford, Robin: my service in Armed Forces Public Affairs (March 6, 2017)
Bibliographic record
Abstract
ABSTRACT: Synopsis: 0:00 – 3:10 – How he first became involved in Public Affairs after his time in the Air Force and became the Base Information Officer for Esquimalt in 1978. 3:10 – 3:47 –The importance of appreciating people from different backgrounds and cultures. 3:47– 5:18- Subsequent training and requirements needed to work at Public Affairs. 5:18 – 12:00 –Working in Public Affairs: Handling the aftermath of the Ocean Ranger, his time as Senior Project Officer; goals in Public Affairs; necessary skills; anti-Nuclear protests. 12:00 -28:00 - Memories of working with other military personnel the press (mentions tour in Cyprus, being in Doha for the Canadian Air Task Group-Middle East, and Hawaii). Also, includes his time as PR point person for servicewomen, who were part of SWINTER. 28:00-45:12 - Releasing news about the military and working the with the press. 45:12-46:50 – Being involved with Norads tracking of Santa Claus Suggested Clip(s) for Archive: 25:00- 28:00 Talks about supporting women entering the military.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.222 | 0.119 |
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".