Detecting Great Power Competition Through Geospatial Analysis: A North American Arctic Case Study
Bibliographic record
Abstract
The current era of Great Power Competition (GPC) between the People's Republic of China (PRC), the Russian Federation (Russia), and the United States is characterized by increased use of \hybrid threats." These are actions, short of military force, that are designed to fall under existing detection and response thresholds and compromise existing security norms and decision making processes. National security scholars and practitioners widely agree that the ability of the United States and its allies to detect and respond to these hybrid threats is limited at best, and that the Indication and Warning (I & W) intelligence function, designed to prevent strategic surprise that fundamentally alters policy, plans, and assumptions about the security environment, has atrophied. This research explores how geospatial science, through the discipline of Geospatial Intelligence (GEOINT) can detect, monitor, and provide I &Wintelligence that prevents strategic surprise from hybrid threats. Specifically, this thesis applied a novel Strategic Intelligence Framework (SIF) to standard I & W intelligence practices to identify, analyze, and visualize PRC activities that carried hybrid threat characteristics within a U.S./Canadian Arctic and circumpolar study area. Through incorporating local spatial context via the Getis-Ord Gi* statistic, as well as the strength of the hybrid threat \signal," this case study successfully identified and mapped higher and lower threat regions using kernel density estimation (KDE) in the form of a Mesoscale Operational Situational Awareness Intelligence Composite (MOSAIC). The success of this case study shows that the SIF and MOSAIC are powerful tools for detecting, analyzing, and warning about the collective impact of hybrid threats.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".