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Record W2792980159 · doi:10.7202/1043125ar

Creating Reading Habits Through Translation in Turkey (1840–1940)

2018· article· en· W2792980159 on OpenAlexvenueno aff
Selin ERKUL YAĞCI

Bibliographic record

VenueMémoires du livre · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicTranslation Studies and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsReading (process)MarginaliaAudience measurementPeriod (music)Variety (cybernetics)PublishingConsumption (sociology)ReadabilitySelection (genetic algorithm)LiteratureHistoryPsychologyLinguisticsSociologyComputer sciencePolitical scienceSocial scienceAestheticsArtLaw

Abstract

fetched live from OpenAlex

Although translated books and readers are visibly and inextricably linked, readers, readers’ expectations, attitudes and habits have only been partially analysed in translation research. In a similar vein, the relationship between translation and reader was rather left undiscovered by scholars studying translation/book/reading history. The aim of this paper is to present the findings of my comprehensive doctoral research on the pioneering role translation played in the history of reading and readers in Turkey between 1840 and 1940 by problematizing the relationship between translation, readers and their reading habits. This hundred year period is characterized by an apparent transformation in the literary production (especially in the number of translated works) and the publishing industry, which created an expansion in the number of readers and the development of new forms to suit the needs and tastes of this new readership. Data from a variety of sources including readers’ letters and auto/biographical accounts will be used in this article to investigate readers, their reading habits and the transformative process they experienced through this reading (r)evolution. In the absence of library records and marginalia due to the inherent characteristics of the period under study, these letters and auto/biographical accounts are of primary importance in providing evidence of what and how the readers were actually reading. Their active involvement in the process (of selection and consumption of translated and/or indigeneous works) is also reflected through the views, experiences and perceptions that are present in these letters and accounts.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.030

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 studies0.0030.003
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.057
GPT teacher head0.281
Teacher spread0.224 · 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

Citations5
Published2018
Admission routes1
Has abstractyes

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