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Record W4402631128 · doi:10.1111/nana.13044

Crafting a national identity: The role of geography textbooks in 1930s Turkey's nation‐building project

2024· article· en· W4402631128 on OpenAlexaff
Hande Gür, Gül Çalışkan

Bibliographic record

VenueNations and Nationalism · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicCyprus History, Politics, Society
Canadian institutionsSt. Thomas UniversityUniversity of Alberta
Fundersnot available
KeywordsIdentity (music)Political scienceNational identitySociologyRegional scienceGeographyLawPoliticsAestheticsArt

Abstract

fetched live from OpenAlex

Abstract This paper investigates geography textbooks of the 1930s in Turkey, contending that geographical knowledge played a pivotal role in shaping nationhood within a modernising state. This study's critical discourse analysis (CDA) of the early republican geography textbooks showcases how (1) Turkey's spatial formation was reimagined in 1930s; (2) defining markers of Turkishness and the Turkish nation were forged through erasures and breakups of minority groups, and (3) the new national identity was “bridged” to the geography of the newly founded Republic. This paper posits that a nation's portrayal of its geography and global positioning is not merely a factual recording, but also a reflection of ideological and political choices. Such portrayals are complete social constructs, inherently influenced by power dynamics and disputes. Beneath the ostensibly impartial depictions of space and spatial relations, invisible power dynamics and assertions underlie the assignments of specific meanings to geographic areas.

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)0.006
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.119

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0100.018
Scholarly communication0.0080.005
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.026
GPT teacher head0.346
Teacher spread0.320 · 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 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

Citations4
Published2024
Admission routes1
Has abstractyes

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