Science at Sundance 2023 <b>Poacher</b> , <i>Richie Mehta, director</i> , QC Entertainment, 2022, 125 minutes. <b>Deep Rising</b> , <i>Matthieu Rytz, director</i> , Roco Films, 2022, 93 minutes. <b>The Pod Generation</b> , <i>Sophie Barthes, director</i> , MK2, 2022, 109 minutes. <b>The Longest Goodbye</b> , <i>Ido Mizrahy, director</i> , Autlook Filmsales, 2022, 87 minutes. <b>Is There Anybody Out There?</b> , <i>Ella Glendining, director</i> , Hot Property Films Ltd, 2023, 87 minutes. <b>Fantastic Machine</b> , <i>Axel Danielson and Maximilien Van Aertryck, directors</i> , See-Through Films, 2023, 88 minutes. <b>The Eternal Memory</b> , <i>Maite Alberdi, director</i> , Micromundo/Fabula, 2023, 85 minutes.
Bibliographic record
Abstract
No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.754 | 0.611 |
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".