Motion/Performance Capture and The Afterlife of The Index. A Reconsideration of André Bazin’s “Myth of Total Cinema”
Bibliographic record
Abstract
The history of film technology is not a progressive and linear march towards the future : it is rather a discontinuous, irregular process where the past returns more often than not. Motion capture and performance capture technologies are particularly indicative examples of this dynamic, as their digital nature is far from opposed to the indexical bias of photography that (according to a widespread doxa) the digital is supposed to gradually supplant. On the contrary, they integrate the index in their own functioning. Moreover, some of the films making use of these devices (for instance Robert Zemeckis’s A Christmas Carol) seem to allegorize this very paradox. André Bazin has often been believed to advocate for a teleological view of history (and of the history of film technology) just because of his idealism. In fact, a close re-reading his writings, particularly of his “Myth of Total Cinema”, shows that his idealism works as an “antidote” against teleological presuppositions.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".