Bibliographic record
Abstract
Anti-Russian sentiment - what some call “Russophobia” - is subtle, but visible in the American foreign policy discourse since the end of the Cold War. Most recently, it can be found in the Obama-era discourse about Russia, despite the positive bump in relations after the so-called “reset” of 2009. This paper contends that, among the many irritants in Russia - U.S. relations, anti-Russian sentiment among the American foreign policy leadership is an understudied phenomenon. Russophobia matters because it is present even at times of promise in the relationship; it impedes striking a “normal” relationship with Russia, and it influences policy decisions. This paper conceptualizes Russophobia, considers the source of its persistence in the American foreign policy discourse, and identifies examples of anti-Russian sentiment among key members of Barack Obama’s foreign policy team through an examination of memoirs and personal reflections about Russia. The paper asserts that anti-Russian attitudes in the American foreign policy discourse throughout the post-Cold War era must be identified and understood in order to gain a better understanding of why forging stronger, mutually beneficial relations with Russia continues to evade American policy makers. Anti-Russian sentiment undermined the Obama - Medvedev reset and, while it is certainly not alone responsible for deteriorating relations with Russia, it helped to perpetuate the downturn in relations and must be identified and better understood. The arguments made in this paper and in the selected citations herein, are based upon non-partisan scholarly inquiry and are not a consequence of the author’s personal or political views.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".