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Record W4221075604 · doi:10.5539/jel.v11n2p104

“Self-Learning French Coursebooks” as Part of French Education in Post Tanzimat Era of the Ottoman Empire

2022· article· en· W4221075604 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Education and Learning · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Methods and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsTurkishPronunciationLinguisticsForeign languageAP French LanguageFrenchHistoryPeriod (music)SociologyArtPhilosophy

Abstract

fetched live from OpenAlex

French teaching in Ottoman Turkey found its actual speed with the Tanzimat period (the political reforms made in the ottoman state in 1839). Until the proclamation of the Republic, and even until the 1950s, French was considered the leading carrier of Western culture and civilization in Turkey, and teaching French was deemed necessary. However, it cannot be said that this was a very successful and sufficient period for French and foreign language teaching in general. Failure to fulfill the primary conditions of language teaching, such as teacher, material, and method, has been the main problem of foreign language teaching. When the lack of schooling is added, “self-learning French” books have emerged as an opportunity for teaching French, although they are not many. The five books discussed in the article, written in Turkish using the Arabic alphabet between 1867 and 1928, mostly describe the basic pronunciation rules, word types, sentence features, and grammatical information of French, starting with the alphabet, in a plain language and style. Although there was a good variety of French-Turkish dictionaries at that time, since economic conditions did not allow everyone to acquire a glossary, and even if there was an opportunity, which dictionary to choose is a different problem, as a standard feature in all of them, the vocabulary parts of the books were kept very wide. Books; It has been seen that both of them are successful when the measures such as showing the pronunciation of French words, grammatical knowledge that is not suffocating, broad vocabulary, the relevance of French and Turkish translation texts, page structure, language, and simplicity of expression are taken into account.

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.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.408
Threshold uncertainty score0.779

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.360
Teacher spread0.346 · 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