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Record W4389949574 · doi:10.5334/johd.169

3D Modelling of Jerusalem’s Maghrebi Quarter

2023· article· en· W4389949574 on OpenAlexaboutno aff
Raphaël Banc-Lévêque, Raffaele Peluso, Marco Cozza, Fabio Bruno

Bibliographic record

VenueJournal of Open Humanities Data · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topic3D Surveying and Cultural Heritage
Canadian institutionsnot available
FundersUniversité Gustave EiffelUniversità Degli Studi di Modena e Reggio Emila
KeywordsQuarter (Canadian coin)GeologyArchaeologyHistory

Abstract

fetched live from OpenAlex

Although Jerusalem is one of the most photographed places in the world, few works have focused on the existence of its former 800-year-old Maghrebi Quarter, located in the shadow of the Western Wall. Founded in 1193 by Saladin’s son, al-Afdal ‘Ali to house Muslim pilgrims from North Africa (today Morocco, Algeria, Tunisia, and Libya), the Quarter was razed by Israeli bulldozers on the night of 10–11 June 1967’. Its 1,000 or so inhabitants were forced to flee within hours. Silenced by the occupation of East Jerusalem as part of the Six-Day War, the existence of the Maghrebi Quarter faded behind the walls of history. This research paper describes how 55 years after its razing, the selection of a corpus of archives, combined with 3D technology and Interprofessional collaborations between historians and 3D designers enable making a forgotten history accessible again. Beyond a discussion on methods and technical process, this research paper aims to highlight the potential applications of the data set and the 3D model not only for scholarly research but also for pedagogical purposes, for instance.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.369
GPT teacher head0.316
Teacher spread0.053 · 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 designSimulation or modeling
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

Citations1
Published2023
Admission routes1
Has abstractyes

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