‘Great Power Competition’ and the Arctic: Origin and Evolution in Media, Governmental and Research Institutes Discourses
Bibliographic record
Abstract
This article seeks to further understanding of the emergence and use of the great power competition (GPC) narrative in the Arctic. Using data gathered between 2010 and 2021 by Factiva, the first part of the analysis identifies the emergence and evolving uses of the GPC term, finding that media outlets played a pivotal role in relaying and keeping this narrative alive in public discourse even after its use subsided in governmental discourse. The analysis then moves to track the GPC discourse with reference to the Arctic specifically; it finds that while it emerged later than the general narrative and originated in the media, usage in this context did not peak concurrently with its use in discussion of global geopolitics or with potentially relevant current events. The second part of the analysis examines how media outlets, government documents, and research institutes understand GPC in the Arctic. We found that the great power competition narrative helped to resurrect discourses of Arctic fear and risk after their waning in the first half of the 2010s. The nature of GPC in the Arctic took familiar contours, being for the most part tied to fears, most conspicuously raised in the early 2000s, regarding resource exploitation, shipping lanes, and militarization. Data is largely from the United States, but contains English sources from American allies, as well as Russia and China.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.011 | 0.023 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".