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Record W2121735429 · doi:10.1162/jcws.2010.12.2.29

When Empathy Failed: Using Critical Oral History to Reassess the Collapse of U.S.-Soviet Détente in the Carter-Brezhnev Years

2010· article· en· W2121735429 on OpenAlexaff
James G. Blight, janet M. Lang

Bibliographic record

VenueJournal of Cold War Studies · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicVietnamese History and Culture Studies
Canadian institutionsBalsillie School of International Affairs
Fundersnot available
KeywordsDemiseSympathyEmpathySoviet unionCommunismPolitical scienceOral historyPolitical economyLawSociologyPsychologySocial psychologyPolitics

Abstract

fetched live from OpenAlex

Drawing on “critical oral history” conferences held after the demise of the Soviet Union, this article seeks to explain why the détente in U.S.-Soviet relations collapsed at the end of the 1970s. Both the U.S. president, Jimmy Carter, and the Soviet Communist Party leader, Leonid Brezhnev, had sought to improve bilateral ties, but instead they found that the relationship deteriorated and then broke down altogether after the Soviet invasion of Afghanistan. The article suggests that neither side had a sufficient appreciation of how the other side perceived the relationship. The authors argue that the critical oral history helped officials on both sides to develop a sense of empathy for how the other side viewed its own interests and objectives. Empathy does not imply any sympathy; instead, it merely entails an effort to understand the other side's perceptions and goals. Presenting excerpts from an oral history conference, the authors argue that greater empathy in the policymaking process might have helped to avoid an outcome that neither side desired.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.041
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.041
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.002
Science and technology studies0.0280.029
Scholarly communication0.0130.013
Open science0.0020.009
Research integrity0.0050.010
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.069
GPT teacher head0.376
Teacher spread0.307 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2010
Admission routes1
Has abstractyes

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