Bibliographic record
Abstract
This paper mainly focuses on Andrei Tarkovsky’s drama films and science fictions to illustrate the way through which his loss and defection are reflected by applying psychoanalysis approach. This paper firstly introduces Andrei Tarkovsky and accented cinema as background information and then conducts discussion concerning different genres of his films respectively. In the discussion of drama films, time and personal memories are strengthened from the perspective of individuals, while in terms of science fictions, this paper puts more emphasis on the ideological issue and political unconscious. Freudian notions of uncanny and doppelgänger are the key tools to do textual analysis, and to understand the obscure implications and metaphors that hinder the viewers from grasping his words between the lines. The theme about homeland and women, usually mother and wife, are distinguishable in Andrei Tarkovsky’s works and conveys his inner appeals. This paper also tries to interpret Andrei Tarkovsky’s film language through aspects such as composition and sound, aiming to provide a deeper and overall discussion of Andrei Tarkovsky and his loss and rebellious spirit in storytelling, which not only offers a glimpse towards that specific historical period but also has a significant and lasting influence on filmmakers around the whole world until nowadays.
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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 | 0.001 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.005 |
| 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".