Bibliographic record
Abstract
In this interview, actor–director Karel Tuytschaever and I examine his first feature film, a critical-creative project entitled Easy Tiger (2022). In it, the meeting between a psychologist (Mickaël Pelissier) and his client (Casper Wubbolts), a deaf man, proves revelatory for them both. The client speaks of a dying man with whom he was deeply in love, which leads him to explore his feelings about the possibility of finding true love. These confessions prompt the psychologist to re-evaluate his relationship with himself and with the world. Tuytschaever and I discuss how he brings together his roles as a director, an actor and an academic, before examining the film and how he communicates his complex vision with us and with his actors. Much of the film involves stillness, and it unfolds in silence. We discuss how Tuytschaever speaks volumes with it, making the film of significant interest for both hearing and other/non-hearing people. Easy Tiger received an Honorable Mention as Indie Feature Film in the Paris Film Awards; and it has earned Best Queer, Best Directing and Best International Film awards at the SENSEI FilmFest. This interview advances scholarship by introducing us to Tuytschaever’s critical work and by examining how it informs his artistic project.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| 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".