Role of public affairs in special operations and missions.
Bibliographic record
Abstract
This thesis sets out to answer the primary question, How do special operations public affairs officers achieve the required balance between the clear and dominant need for operational security before, during, and after an operation while maintaining the public affairs (PA) imperative of Maximum Disclosure, Minimum Delay? In short, can the special mission community move beyond the no comment or I can neither confirm nor deny approach and adopt a more sophisticated and effective PA plan. Along the way the thesis explored concepts, like Just War, and brought to light the unique societal questions posed by the government's need for secret military units and capabilities and the problems these units pose for a free and open society. Four allied nations--Britain, Australia, New Zealand and Canada--have all experienced public debate about their country's secret military units and have struggled to reevaluate their respective secrecy policies. In the aftermath of the terrorist attacks of 11 September 2001, the American public has not yet joined this debate. When it does, will the Department of Defense's PA policy of not discussing special operations missions withstand public and political scrutiny?
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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.024 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".