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Record W4242091711 · doi:10.24908/iqurcp.8547

Borders, Ideology, Geography and Maps – The Case of Israel

2018· article· en· W4242091711 on OpenAlexvenueno aff
Evan Perlman

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicJewish and Middle Eastern Studies
Canadian institutionsnot available
Fundersnot available
KeywordsIdeologyPoliticsJudaismIdentity (music)PopulationDoctrineGeographyArgument (complex analysis)National identityPolitical scienceSociologyLawArchaeologyAesthetics

Abstract

fetched live from OpenAlex

Although there are dozens of countries with present day border disputes, few have received such unrelenting international focus as Israel. Maps, cartography and geographic education support the developing doctrine of national boundaries that form collective national identity and ideology. Geographically, throughout the past century, the borders of Israel have become a melding of the phenomena of national identity with physical territory – also referred to as territorial socialization. My paper argues that Israel’s use of geographic description of borders specifically through cartography over time is an example of how boundaries are a powerful tool in the naturalization of ideology of Jewish Israelis. This argument is analyzed by examining historical and biblical cartography, territorial evolution, geography curriculum and textbooks, the Atlas of Israel and mental mapping by citizens. Varying portrayals of Israel’s historical, biblical, natural and political boundaries creates an ambiguous definition of Israel’s borders for citizens. In turn, this importantly shapes the present day religious and seculargeographies of the population of Israel as well as the political behaviours by the democratically representative Israeli government.

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)0.002
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.029
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0090.024
Scholarly communication0.0080.004
Open science0.0010.003
Research integrity0.0020.002
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 head0.099
GPT teacher head0.406
Teacher spread0.308 · 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 designTheoretical or conceptual
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
Published2018
Admission routes1
Has abstractyes

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