Bibliographic record
Abstract
The cinema of Andrei Tarkovsky stands at the zenith of high-modernist cinema. Amongst the many technical achievements that characterize Tarkovsky’s total art approach to cinema, a few stand out: the fragmented and free floating diegeses of his films, which often defy the teleology of metanarratives; a wide array of poetry and paintings, grafted onto the plot as philosophical subtext; long takes, intricately choreographed, with many optical illusions; an insistence on reducing the role of color in order to enhance the "expressiveness" of the image (Johnson 188); and, finally, a spare cinematic score that is often mainly a "refrain" to accentuate emotional climaxes (Tarkovsky 158). Thematically, a number of key metaphysical, autobiographical, and historical motifs also recur in his films. The ethical crisis of the human in a universe devoid of humanistic faith; the playful authority of memory over subjectivity, the simultaneous and conflicting alterity and interiority of Nature, and the dilemma of the instrumentality of technology and reason to the capitalistic ratio exemplify some of his main concerns.
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 | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".