From an Empire to a Nation State: Importing the Concept of Nation into Ottoman/Turkish Thinking
Bibliographic record
Abstract
During importation processes of concepts, the target context and the agents involved in these processes are central and shape the imported ideas. Hereby, translation, both in its narrow and broader senses, plays an important role. The aim of this article is to present preliminary research results on the importation process of the concept ofnationinto the Ottoman/Turkish culture as the target culture. The article provides research results gained from the analysis of dictionaries as well as of texts written by important figures of Turkish nationalism during the last decades of the Ottoman Empire. The research covers first-hand analysis of key texts by Yusuf Akçura and Ziya Gökalp whereby the use of the concept of ‘nation’ by other key figures are discussed on the basis of secondary sources. The analysis also includes translations. This study, which is linked to a study on the concept of ‘culture,’ was based on an interdisciplinary approach relying on the perspectives and notions of translation studies and on methodology developed in conceptual history. The theoretical framework and methodology adopted in this study are exposed in the first part, whilst the second part presents and discusses the research results.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".