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Record W1991343154 · doi:10.3366/hls.2012.0045

Israel, 1948 and Memoricide: The 1948 Al-‘Araqib/Negev Massacre and its Legacy

2012· article· en· W1991343154 on OpenAlexaff
Nasser Rego

Bibliographic record

VenueHoly Land Studies · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicJewish and Middle Eastern Studies
Canadian institutionsYork University
Fundersnot available
KeywordsPersecutionHistoriographyConstruct (python library)ColonialismRefugeeHistoryPoliticsEthnic CleansingAncient historyLawGender studiesPolitical scienceSociologyArchaeology

Abstract

fetched live from OpenAlex

In September 1948 fourteen young and middle-aged Palestinian Bedouin men from the Naqab (Negev), most tending to fields and livestock, were rounded up on a Zionist army vehicle and driven to the abandoned home of refugee ‘Odeh al-Qawasmeh in al-‘Araqib and summarily executed. More recently, on 27 July 2010, the entire village of al-‘Araqib was demolished by Israeli security forces in what locals have termed ‘the new Nakba’. This article attempts to construct an ‘authentic history' around al-‘Araqib by linking the ‘cleansing’ operations in 1948 and those in 2010, by weaving together native accounts and silences in the historical record. It challenges contemporary historiography's tendency to discount native testimony and to banalise violence in this case of ‘ongoing colonialism’. By drawing in the example of the political persecution of a local activist attempting to construct such an authentic history, the article draws attention to the various currents that aim at effecting a memoricide of al-‘Araqib.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.010
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.073
GPT teacher head0.342
Teacher spread0.269 · 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

Citations16
Published2012
Admission routes1
Has abstractyes

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