Kuruluşundan günümüze Marmara Üniversitesi Güzel Sanatlar Fakültesi Sinama- TV Bölümün’de eğitim “tavanarasındaki düşler”
Bibliographic record
Abstract
Sinemanın keşfedildiği 1895 yılından bu yana sinemada olduğu gibi, sinema eğitiminde de değişik yaklaşımlar ortaya çıkmıştır. 1919 yılında Rusya’da kurulan Rusya Federasyonu Devlet Gerasimov Sinematografi Enstitüsü ile başlayarak, bir çok sinema okulu kurulmuş, farklı eğitim tarzları ortaya çıkmıştır. Türkiye’de ise sinema eğitimi 1975 yılında başlamıştır ve 1982 yılında kurulan Marmara Üniversitesi Güzel Sanatlar Fakültesi Sinema-TV Bölümü ülkemizdeki en köklü sinema bölümlerinden biridir. Çeyrek yüzyılı geride bırakmış bu kurum nasıl ve kimler tarafından kurulmuştur? Kuruluş amacı nedir? Türkiye’deki dünyadaki yeri ve önemi nedir? Tüm bu sorular sadece bir sinema bölümü ile ilgili değil, genel olarak “sinema eğitimi” ile ilgilidir. Since when cinema was discovered in 1895, there were different approachs about cinema and cinema education. Many film schools were founded and different education methods were developed since inception of Gerasimov Institute of Cinematography (VGIK) in 1919. In Turkey, cinema education started in 1975 and since 1982, Marmara University Faculty of Fine Arts Cinema-TV Department constitutes one of the most fundemental cinema schools in our country. How and from who was founded this cinema school which continues to educate during the quarter of a century. What was its purpose to be founded? What is its importance in world and in Turkey? This questions are not just about a cinema department, but they are about “cinema education” generally.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.004 |
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".