Bibliographic record
Abstract
Abstract This article analyzes two recent streaming series—the third season of Tom Clancy's Jack Ryan (Prime, USA, 2022) and the limited series Treason (Netflix, UK, 2022)—as aspects of “Cold War II,” the increasingly common term for the resurgence of anti-Russian attitudes and stereotypes in Anglophone cinema and television that has been apparent since 2010. These two series reflect a shared fear of Russians and offer interesting and illuminating points of comparison, especially regarding their definitions of the threat (internal or external), the battleground (at home or abroad), and strategies for confronting the enemy (shoot ’em up or run and hide). At the same time, these series reflect different national concerns, with Jack Ryan 3 as one of many US-produced spy thrillers that trumpet aggressive, offensive action in a way that deflects attention from the country's serious divisions and protracted domestic crises. Treason, on the other hand, engages with British concerns over corruption in the UK's police and security services. Finally, the series’ differing treatments of the relationship of the not-so-distant past to present dangers (real and perceived) is also noteworthy, if puzzling; the demise of the Soviet Union is central to the crisis in Jack Ryan 3 but only glancingly mentioned in Treason.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".