Belgesel sinemada kent olgusu ve “Yolcu”
Bibliographic record
Abstract
NDEKİLER………………………………………………………………...II ÖZET………………………………………………………………………….IV ABSTRACT…………………………………………………………………..VI GĐRĐS……………………………………………………………………..….VII 1. BELGESEL SĐNEMADA KENT OLGUSU………………………………..1 1.1.Belgesel Sinema Nedir?.....................................................................1 1.2. Belgesel Sinemanın Amacı ve Đlkeleri..............................................3 1.3. Belgesel Film Türleri ……………………………………………...5 1.3.1 Toplumsal Belgeseller………………………………………..5 1.3.2 Doğa Belgeselleri…………………………………………….6 1.3.3 Bilimsel Belgeseller…..……………………………………...6 1.3.4. Propaganda Belgeselleri……………………………………..7 1.4. Belgesel Sinemanın Doğusu…………………………………….....8 1.4.1. Gündelik Hayatı Belgeleme’nin Tarihi……………………...8 1.4.2. Hareketli Görüntüye Geçis ve Đlk Belge Görüntüler……......9 1.4.3. Đlk Belge Görüntülere Örnek Olarak Lumiere Filmleri ve Lumiere Filmlerinde Kentin Kullanımı…………………….11 1.5. Belgesel Sinemanın Gelisimi……………………………………..13 1.5.1. Robert Flaherty ve Nanook of The North………………….13 1.5.2. John Grierson ve Đngiliz Belge Okulu…………………….15 1.5.3. Walther Ruttmann’dan Senfonik Bir Kent Belgeseli: Berlin, Bir Büyük Kent Senfonisi………………………….18 1.6.Belgesel Sinemada Kentin Karaktere Dönüsümü…………………20 1.6.1. Belgesel Sinemanın Đlk Kuramcısı: Dziga Vertov ve Sinema Göz Kuramı……………………………………….21 III 1.6.2. Dziga Vertov’un Kameralı Adam Filmi ve Belgesel Anlatıda Kentsel Doku…………………………...25 1.6.3. 1960’lar Gerçekçilik Akımı ve Konulu Filmlerde Belgesel Yapının Kullanımı…………………28 1.6.3.1. Jean Luc Godard’ın Serseri Asıklar Filminde Toplumsal, Sosyal ve Ekonomik Ortamı Belgelemek Adına Kentin Hatıralanması……………………….32 1.7. Belgesel Sinemanın Modernitenin En Önemli Karakteri Kentin Belgelenmesinde Đslevi…………………………………………..35 1.7.1. Gündelik Hayatın Görsel Belgesi Bağlamında Kent Belgeselleri…………………………………………..35 1.7.2. Günümüz Türk Sinemasından Đstanbul Üzerine ‘Senfonik’ Bir Kent Belgeseli: Đstanbul Hatırası…………..37 2.YOLCU FĐLMĐ 2.1.Kavram: Kent…………………………………………………….41 2.1.1.Gösteri Alanı Olarak Kent………………………………....41 2.1.2. Sinematografik Olarak Kent……………………………….45 2.2.Filmin Olusum Süreci…………………………………………….49 2.2.1.Konu………………………………………………………..49 2.2.2.Amaç………………………………………………………..49 2.2.3.Karakter:Jonglör…………………………………………….51 2.3.Filmin Temel Öğeleri…………………………………………….52 2.3.1.Çekim Mekanları…………………………………………...52 2.3.2.Görüsme ve Röportaj Yapılan Kisiler………………………54 2.3.3.Teknik Dil…………………………………………………...54 2.3.4.Kurgu………………………………………………………..55 SONUÇ……………………………………………………………………….57 KAYNAKÇA..……………………………………………………………….60 IV
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.052 | 0.013 |
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".