Pearl Harbor and American Intelligence Culture: Cultural Symbols in American Intelligence Discourse Communities, Meta-Theoretical Lenses and Multi-Perspective Approaches
Bibliographic record
Abstract
Ideas of the world persist long beyond the contexts that gave rise to them.In intelligence, as in strategic culture, these persisting ideas form a culture which shapes the intelligence system.Persisting ideas can come to form part of a national culture that ultimately shapes the actors, interests, and decision-making of intelligence stakeholders.This thesis illuminates how specific American cultural symbols influence and reflect U.S. intelligence ideas, discourses, policies, and practices, thereby contributing to a distinctly "U.S. intelligence culture."As a result, this dissertation demonstrates that national cultural symbols are not epiphenomenal: These symbols tangibly influence the thought and action of diverse audiences both within and outside the formal U.S. intelligence community.Through an analysis of national and institutional definitions, norms, narratives, and warrants, the contours of a uniquely American intelligence culture emerge.A test case of how Pearl Harbor functions as a powerful cultural symbol among intelligence discourse communities illustrates national culture's influence vis-à-vis policy decisions, intelligence reform, institutional openness, and intelligence education.Ultimately, this dissertation's meta-theoretical lens and multi-perspective approach productively bring together conceptual resources from the fields of Intelligence Studies, Strategic Studies, and International Relations contributing to the development of "intelligence culture" as a theoretically sound and useful approach to studying intelligence-related phenomena.
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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.008 | 0.033 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".