Bibliographic record
Abstract
Director and co-writer Matteo Sanders’ ambitious short film “On the Edge” (2022), or “Am Grat,” centres on the relationship between two siblings—Paul (co-writer Tobias Resch) and Felix (Tobias’ real-life brother Noah)—as they take on the Eisenerzer Reichenstein mountain. The ascent, already physically demanding, is made more so by severe weather conditions and by the multiple sclerosis (MS) that’s affecting Paul. Sanders and Resch nevertheless present a positive outlook: they explore how the brothers reconfigure their relationship, and they do so through the characters’ (and actors’) silences as much as their speeches, their uncertainties as much as their certainties. In this interview, Sanders, Resch, and I examine the filmmakers’ depiction of these characters; their research into, and their imaginative approach to, MS; the challenges of filming on the often incredibly foggy Alps; and their use of the seemingly impenetrable weather condition as a metaphor. This interview offers insights into the challenges—as well as the rewards—of filming in this environment, and it celebrates “On the Edge,” which has screened at dozens of festivals internationally and which has now been recognized by Vimeo as a Staff Pick.
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 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.012 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.012 |
| 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".