A SYSTEMATIC ANALYSIS OF AUTEUR THEORY IN CINEMA IN GRADUATE THESES IN TURKEY
Bibliographic record
Abstract
This study was carried out in order to reveal the current outlook of the postgraduate theses in the Department of Radio, Television, and Cinema in the YÖK Thesis Centre by using the systematic search method and to help future studies by providing data. For this purpose, the YÖK Thesis database was searched systematically by using the keywords ‘auteur’ and ‘auteur theory’ at the end of December 2023, and a total of 252 postgraduate thesis studies were reached. After limiting the total number of outof-field theses, the sample number was reduced to 48. The distribution of the theses on auteur theory between 2002 and 2023 according to years, their distribution on the basis of universities, the distribution of the language in which the theses were written, the gender distribution of the thesis authors, the distribution of the advisor profile of the theses, the directors examined within the framework of auteur theory and the country cinema distribution of the directors were analysed and evaluated. In the study, 48 theses were analyzed, 46 of which were master’s theses and 2 of which were doctoral theses. Between 2018 and 2023, there was an increase in the number of theses analyzed using auteur theory. Marmara University, Atatürk University, and Ankara University were the universities with the highest number of theses. In the distribution of directors by country, there were 26 Turkish directors, 4 Iranian and American directors, 2 French, Kyrgyz, and Mexican directors, and 1 Kurdish, Japanese, British, Canadian, Austrian, and Macedonian director.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".