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Record W7018102832

Contested sites in Jerusalem the Jerusalem Old City Initiative

2018· article· en· W7018102832 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicJewish and Middle Eastern Studies
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Work (physics)Corporate governanceGround zeroSecurity councilControl (management)
DOInot available

Abstract

fetched live from OpenAlex

Contested Sites in Jerusalem is the third and final volume in a series of books which collectively present in detail the work of the Jerusalem Old City Initiative, or JOCI, a major Canadian-led Track Two diplomatic effort, undertaken between 2003 and 2014. The aim of the Initiative was to find sustainable governance solutions for the Old City of Jerusalem, arguably the most sensitive and intractable of the final status issues dividing Palestinians and Israelis. This book examines the complex and often contentious issues that arise from the overlapping claims to the Temple Mount/Haram al-Sharif, the role of UNESCO, and the major implications of the JOCI Special Regime for such issues as archaeology, property, and the economy. Part One is dedicated to holy sites - ground zero of the Israeli-Palestinian conflict, a point reinforced by the Fall 2014 disturbances which threatened to spiral out of control and engulf Palestinians and Israelis into yet another wave of violence. Part Two of the volume contains studies on archaeology, property, and economics that were written after the completion of the Special Regime model, specifically to address in depth how a Special Regime would deal with each of these three important areas. Contested Sites in Jerusalem offers an insightful explanation of the enormous challenges facing any attempt to find sustainable governance and security arrangements for the Old City in the context of a peace agreement between the Israelis and the Palestinians

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)0.001
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 score0.413
Threshold uncertainty score0.846

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.098
GPT teacher head0.338
Teacher spread0.239 · 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
Published2018
Admission routes1
Has abstractyes

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