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Record W2957416931 · doi:10.25071/2369-7326.40308

“A Meat Locker in Hebron”: Meat Eating, Occupation, and Cruelty in To the End of the Land

2019· article· en· W2957416931 on OpenAlexaffvenue
Aaron Kreuter

Bibliographic record

VenuePivot A Journal of Interdisciplinary Studies and Thought · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsYork University
Fundersnot available
KeywordsCrueltyTortureWifeIdeologyGrossmanJudaismLawDehumanizationState (computer science)CriminologySociologyHistoryLiteraturePolitical sciencePhilosophyArtTheologyPoliticsHuman rightsEconomics

Abstract

fetched live from OpenAlex

In this paper, I explore the connections between meat-eating, cruelty, and the Israeli/Palestinian crisis in Israeli author David Grossman's 2008 novel To the End of the Land (translated from the Hebrew in 2010 by Jessica Cohen). Using the radical vegetarian-feminist theories of Carol J. Adams, I argue that in the novel, Grossman reveals how the Israeli nation-state's treatment of the occupied Palestinian people is part and parcel of the same ideological construct that allows its citizens to consume the flesh of dead animals; if a nation can eat meat, it can dehumanize and oppress its unwanted others. In particular, I look at a pivotal moment in the novel, where the protagonist Ora's son's military unit leaves an elderly Palestinian man chained up and suffering in a Hebron meat locker; I locate this event as the most important physical space in a novel preoccupied with space, land, and physicality. I also look at another example of a Jewish author grappling with the cruelty of eating meat, the Yiddish writer Isaac Bashevis Singer's short story "The Slaughterer." Finally, I interrogate the idea, put forward by Todd Hasak-Lowy, that Grossman is less concerned with the sufferings of the Palestinian people than he is the sufferings of the stoic Israeli, forced to make compromising moral choices.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score0.902

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.024
GPT teacher head0.355
Teacher spread0.331 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2019
Admission routes2
Has abstractyes

Explore more

Same venuePivot A Journal of Interdisciplinary Studies and ThoughtSame topicGeographies of human-animal interactionsFrench-language works237,207