Sensations and cinema: Reframing the real in democracy and education
Bibliographic record
Abstract
In the film Sans Soliel, Chris Marker challenges received wisdoms with regard cinematic production of real worlds and real people. In Marker’s techniques, Jacques Rancière observes an intensely political, highly accessible, art form that leads to a theorisation of cinema for its democratic and educational functions. In this paper we take up Rancière’s interest in the democratic and educational functions of cinema through a reading of three films: Sans Soliel, Minority Report, and After Yang. Marker’s essayist cinema produces an uncanny experience of anthropological irony, and a mode of rethinking imperialism, revealing stories of communities that typically do not get told. Spielberg’s film adaptation of Philip K. Dick’s story is a cautionary contemplation on the ethics of the future of a police force that has access to visions of the future. Kogonada’s poetic lens muses on what it is to be human, what it is to be a family, and what it is to be a child and a parent negotiating complexity, loss, and identity. Each film is of interest here for the openness with which they engage thinking about democracy and education. They are democratic and educational precisely because they do not tell us what to think about democracy and education. Each film at the same time provides insight into Rancière’s thinking about the functions of cinema in producing senses of politics.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".