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Record W2907636917 · doi:10.1007/978-3-319-97999-1_6

The Rise of Operation Reinhard

2018· book-chapter· en· W2907636917 on OpenAlexaff
Nestar Russell

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicPsychology of Social Influence
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBureaucracyGovernment (linguistics)RationalityHistoryPolitical scienceEngineeringLawPhilosophyPolitics

Abstract

fetched live from OpenAlex

Abstract In this chapter, Russell details what evolved into the large-scale gassing programs in the East, with a particular emphasis on Operation Reinhard—the extermination of Jews in the city ghettos of the General Government. What follows shares much in common with the pattern of escalation depicted in the previous chapters: initially low rates of killing, top-down pressure to increase those rates, the application of formal rationality from the bottom-up (increased experimentation, bureaucratization, and the honing of a less stressful killing process), resulting in increased kill rates that only served to stimulate new top-down pressures to meet new and even more ambitious goals, thus occasioning an ever-expanding cycle of destruction.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.007
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.010
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0010.006
Insufficient payload (model declined to judge)0.0070.001

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 head0.047
GPT teacher head0.361
Teacher spread0.315 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2018
Admission routes1
Has abstractyes

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