Cinema as Volcano: Thinking Cinema Through the Volcano with Malena Szlam, Werner Herzog and Jean Epstein
Bibliographic record
Abstract
Abstract In “Cinema Seen from Etna”, Jean Epstein makes an enigmatic comparison between cinema and the volcano. In witnessing the exploding Etna, Epstein proclaims that he saw cinema itself. This essay examines this connection between cinema and the volcano through an exploration of Werner Herzog’s documentary Into the Inferno (2016) and Malena Szlam’s geological films ALTIPLANO (2018) and MERAPI (2021). Drawing on literature in environmental humanities – especially those on the lithic and the elemental – I think the cinema through the volcano to arrive at the proposition that cinema, at least a certain kind of ‘volcanic cinema’, is a means of approaching something we might call ‘volcanic thinking’. This is a thinking alongside the elements, one that bears particular affinity with the sometimes explosive churning of rock and fire, a thinking that is necessary and critical for our volatile age.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".