Bibliographic record
Abstract
This paper examines how music and juxtapositions can ground a story in a longer history where the potential of images and cutting points become a dialectics of point, counter-point, and fusion in a revisitation of archetypal images and as a co-authorship of reception. A visual dialogue evolves in the film Shchedryk (2014) through a remediation of scenes from Sergei Eisenstein’s Battleship Potemkin (1925), Alexander Dovzhenko‘s Earth (1930) and Norman McLaren’s experimental film Synchromy (1971). People who do not have recourse to the dominant culture are through recipient-co-authorship able to replay things in more sophisticated ways. Judith Butler’s idea of the performative and of subjects re-performing an injury (Butler 1993) can be introduced to the multi-screen experience. Foregrounding the wounding aspect as visual images is about ‘bad pleasure’ (O’Brien & Julien 2005). If realness is a standard by which we judge any performance, what makes it effective is its ability to compel beliefs and embody and reiterate norms (Butler, 387). Image Credit: Frame from Shechedryk, directed by Kalli Paakspuu
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 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".