Adaptation and its Illusions: Don Quijote in the films of Vlado Kristl and Albert Serra
Bibliographic record
Abstract
Ovaj rad izlaže temeljitu analizu dvije filmske adaptacije Bistrog viteza Don Quijotea od Manche u režiji Vlade Kristla i Alberta Serre – Don Kihota i Viteške časti. Polazeći od usporedbe ovih filmova s drugim adaptacijama iz opusa njihovih autora, rad ukazuje na jedinstvene mogućnosti koje Cervantesov roman pruža svojim adaptatorima, a koje uvelike proizlaze iz toga što, kako je uočio André Bazin, Don Quijote nadilazi roman iz kojeg potječe. Don Kihot i Viteška čast stoga se odmiču od konkretnih zbivanja iz romana, postavljaju zvuk i sliku u prvi plan te njima promišljaju sam čin ekranizacije, odnosno njegovu artificijelnost. Animirani Don Kihot prikazuje Don Quijotea kao šuplju vodovodnu cijev (pokretni zvučni crtež) kako bi ukazala na njegovu rekonceptualizaciju u drugom mediju, ali i njegovu iskonsku šupljinu, dok se Viteška čast usredotočuje na tijelo glavnog glumca i kostimografiju kojom ga se pretvara u lik bez kojeg iluzija igranog filma ne bi postojala. Štoviše, oba filma također inzistiraju na odvojenosti slike i zvuka, putem čega na površinu izlaze presudne razlike između tretmana iluzije u književnosti i na filmu. Posljednja je dionica analize u oba slučaja posvećena citatima pomoću kojih Kristl i Serra svoje autorefleksivne filmove sukobljavaju s drugim autorefleksivnim umjetničkim djelima te izravno uspostavljaju intertekstualni dijalog ne samo s vlastitim književnim predloškom, već i drugim slikama.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".