“Self-Learning French Coursebooks” as Part of French Education in Post Tanzimat Era of the Ottoman Empire
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".