Constructing reality through language. Russia in the Western Arctic discourse
Bibliographic record
Abstract
The Arctic has often been viewed as a region insulated from broader global conflicts, a concept known as ‘Arctic exceptionalism’. However, Russia’s full‑ scale invasion of Ukraine in 2022 has challenged this notion, leading to growing tensions and increased militarisation. This study uses a poststructuralist framework to analyse the Arctic strategies published by Western Arctic states between 2006 and 2024, treating these strategies as key speech acts that construct political meaning. Discourse analysis, facilitated by AntConc software, examines how Western Arctic states frame Russia and how these narratives have evolved. The findings reveal a shift from portraying Russia as a cooperative partner to an increasingly militarised and expansionist actor. This shift reflects contrasting approaches – Western states focus on multilateralism and international law, while Russia emphasises sovereignty and military power. The poststructuralist approach highlights how discourse actively constructs Arctic political realities, influencing power dynamics and regional stability. Future Arctic governance depends on resolving broader political tensions, but meaningful re–engagement with Russia remains uncertain. Sustaining multilateralism and adherence to international law will be crucial to counter destabilising narratives and support a cooperative and peaceful Arctic.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.025 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".