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Record W4237683516 · doi:10.1353/gsp.2011.0058

Churchill in Munich: The Paradox of Genocide Prevention

2008· article· en· W4237683516 on OpenAlexvenueno aff
Robert Melson

Bibliographic record

VenueGenocide Studies and Prevention · 2008
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Conflict Studies
Canadian institutionsnot available
FundersStrong
KeywordsGenocideIronyTreasureThe HolocaustHistoryLawSpanish Civil WarWorld War IIPolitical scienceSociologyCriminologyLiteratureArt

Abstract

fetched live from OpenAlex

A catastrophe averted is likely not to be viewed as a catastrophe.A predicted event that fails to materialize is a non-event, something that did not happen, and politicians who expend wealth and lives on something that fails to happen cannot expect to reap the rewards of their decisions.Quite to the contrary, politicians who spend lives and treasure to prevent catastrophes such as genocide are likely to be vilified and punished for their efforts: to the extent that their actions succeed in averting a catastrophe, there is no proof of their success, only of the costs of their efforts.This last point is especially intriguing, and it goes to the heart of the paradox of genocide prevention.Consider the famous case of Winston S. Churchill.Had he, instead of Neville Chamberlain, been Britain's prime minister in the 1930s, and thus gone to Munich to meet Adolf Hitler in 1938, there is a good chance that World War II would have been averted and the Holocaust prevented.The irony is that had Churchill been successful in preventing war and genocide, the British public would not know about his triumph, because there would be no evidence for it.All the public would be sure of was that Churchill had brought the world to the brink of war, and he would be blamed for that.The further irony is that, had Churchill succeeded in preventing the war, he might have gone down in history as an erratic warmonger rather than as the greatest war leader of the Western world.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.735
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.250
GPT teacher head0.490
Teacher spread0.240 · 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 teacher head, not a consensus.

Study designObservational
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

Citations0
Published2008
Admission routes1
Has abstractyes

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