The coverage from Russian press agencies of the Greenland purchase story
Bibliographic record
Abstract
Russian media outlets controlled by the Russian state have been known to disseminate dis/misinformation or fake news, especially about rivals such as Western countries. We investigate if this was the case for an incident in which the then-President of the United States Donald Trump declared his administration’s interest in purchasing Greenland. Using Factiva, we collected and reviewed all English-language articles published on this controversy by Russian news outlet Sputnik in August 2019 and found that dis/misinformation was not at play in this instance. However, we did observe that Sputnik deployed numerous strategies to aggravate tension and confrontation between the United States and Denmark and employed specific frames to take part in informational competition with Western states, including Arctic ones. This study is important in order to identify how strategies other than outright lying contribute to Russia’s foreign policy goals, and in turn to identify how best to counter these strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".