The Colder War: Predicting Government Deception in the Arctic Conflict
Bibliographic record
Abstract
As rising temperatures increase the rate of polar ice cap melt, the Arctic is rapidly becoming a region of significant geopolitical interest, creating the potential for a revived Great Powers struggle. A key consideration surrounding potential conflict for democracies is the role of official government secrecy in foreign policy decision-making. This particular study examines the intersection of the two topics, creating a model of understanding and predicting U.S. government deception in conflicts. Specifically, this research identifies the gap between internal U.S. government knowledge and publicly broadcasted messaging surrounding the justifications for military and covert action. By analyzing case studies of secrecy scandals from the Cold War and comparing theoretical schools of government deception, a comprehensive model of understanding can be extrapolated to future conflicts. I argue that Americans can expect the government to publicly communicate a defensive posture and concern for national security interests in the Arctic, while privately prioritizing the economic and hegemonic gains that dominance in the Arctic could produce.
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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 | 0.005 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".