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Record W4302287909 · doi:10.1080/13527258.2022.2131880

‘The Jewish people in their homeland’: the discursive mechanisms of Israel’s cultural heritage policy

2022· article· en· W4302287909 on OpenAlexaff
Rudy Kisler

Bibliographic record

VenueInternational Journal of Heritage Studies · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Heritage Management and Preservation
Canadian institutionsMcGill University
Fundersnot available
KeywordsHomelandCultural heritageCultural heritage managementContext (archaeology)Industrial heritageNarrativeConventionPolitical scienceHistoriographyJudaismPlan (archaeology)ValuesDocumentationInterpretation (philosophy)Public administrationSociologyVariety (cybernetics)HistoryEnvironmental ethicsLawPoliticsArchaeologyArtLiterature

Abstract

fetched live from OpenAlex

This article critically examines the discursive structure, operational mechanism, and historical narratives embedded in the Heritage Plan, Israel’s official cultural heritage policy since 2010. One central stated goal of the policy is to strengthen Israel’s heritage infrastructure to convey the idea of ‘the Jewish people in their homeland’. This article uses the concept of the ‘Jewish people in their homeland’ to explore the guiding premises of the policy. By analysing a variety of data sources (such as policy documentation and interviews with the policy’s architects), this study investigates the discourse which constitutes and is constituted by the Heritage Plan. The findings suggest that the Heritage Plan is guided by international heritage conservation policies such as UNESCO’s World Heritage Convention which have been adapted to the Israeli context. Specifically, this adaptation is derived from a particular interpretation of Zionist historiography, which in turn guides the goals, selection criteria for heritage sites and ultimately the historical narratives presented by the Heritage Plan.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.611
Threshold uncertainty score0.446

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 science0.0010.000
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 head0.063
GPT teacher head0.291
Teacher spread0.228 · 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 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

Citations3
Published2022
Admission routes1
Has abstractyes

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