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Record W1971201111 · doi:10.3138/carto.49.1.2025

Disordered Ordering: Mapping the Divisions of the Ottoman Empire

2014· article· en· W1971201111 on OpenAlexvenueno aff
Karen Culcasi

Bibliographic record

VenueCartographica The International Journal for Geographic Information and Geovisualization · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicGeographic Information Systems Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEmpireNegotiationOttoman empireScholarshipFormative assessmentHistoryFirst world warPolitical scienceAncient historyLawSociology

Abstract

fetched live from OpenAlex

The division of the Arab territories of the Ottoman Empire following World War I was a formative and highly contentious process in which maps played an integral role. The British and French delegates who parcelled out the Ottoman Empire created and altered maps in a succession of treaties and conferences that would lead to the reordering of these territories into new imperially mandated states. This article examines the maps that British delegates used and created during the war years and in peace negotiations over the Ottoman Empire. Expanding on recent scholarship framing maps as processes rather than static documents or representations, the author applies the “maps as process” framework to historical maps, a broad category of mapping yet to be addressed by this growing body of literature, to highlight the disordered yet interconnected ways in which seven British archival maps emerged from these negotiations and ultimately helped to reorder this area of the world.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.311
Threshold uncertainty score0.618

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0060.010
Science and technology studies0.0040.009
Scholarly communication0.0090.005
Open science0.0010.004
Research integrity0.0010.001
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 head0.023
GPT teacher head0.299
Teacher spread0.276 · 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 designNot applicable
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

Citations6
Published2014
Admission routes1
Has abstractyes

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Same venueCartographica The International Journal for Geographic Information and GeovisualizationSame topicGeographic Information Systems StudiesFrench-language works237,207