Bibliographic record
Abstract
What if subconscious somatic impulses were to be heard? What if these impulses were a means to communicate silently and non-categorically? What would a film that follows these bodily impulses look and sound like? To answer these questions, I have devised a cinematic production and postproduction process that prioritizes neither verbal language nor mimesis but, instead, subliminal subtext. Through a form of worldbuilding and storytelling that focuses on the silences between the bodies in front of the camera, I expose a hidden somatic language and capture it in moving images. I trace tensions and affective expressions that are visible within the body yet often buried underneath words and deliberate gesturing. As part of my production method, I analyze performers’ synchronized sonic breath data with the help of machine learning and custom-made wearable stethoscopes I have developed to foreground the intricate physical expressions that tend to be unseen yet form a distinct language unique to the encounters and intimacies exchanged between bodies. Much like a polygraph, this process exposes corporeal impulses and reveals the nuances bodies communicate when they cannot hide behind words or control their reflexive reactions. I then actively “co-edit” with the algorithm and disregard the traditional shot - counter shot logic to, instead, privilege patterns of kinaesthetic communication that demand a different rhythm. Kinaesthetic negotiation does not organize around questions and answers nor around the notion of turn-taking as dictated by verbal communication. Instead, bodies exchange somatic information simultaneously and continuously (Barsalou 2003, Feldman Barrett 2017, Bateson 1977). In order to decipher patterns within somatic communication, each exchange is regarded as singular and specific to the interactions of the particular bodies involved. Unlike the current trend of using deep learning by relying on vast and inevitably misrepresentative datasets, my application of AI uses the specific data of the people involved in the filmmaking process and is not exposed to biased datasets. This presentation discusses how somatic dynamics, once deciphered as a form of language, reveal affective patterns in non-verbal communication that are key to how bodies move one another on screen. It introduces an application of AI in cinema which attends to and augments corporeal nuance.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".