Bibliographic record
Abstract
Distributed by Institute of Time, Utrecht, NLProduced by Firat SezginDirected by Deniz Tortum2020, Streaming, 71 mins Phases of Matter is a meditative journey through a teaching hospital in Istanbul. It is an artistic and abstract contemplation of life and death explored through the microcosm of the aging Cerrahpasa Hospital. Phases of Matter is non-narrative, with no voiceover, instead exploring these themes through vignettes of hospital life. The camera follows various people and scenes around the hospital throughout the day. It is an honest and unflinching portrait of life in a hospital that captures both the mundanity and importance of medical work. Scenes of hospital workers chatting over lunch or playing on their phone are contrasted with doctors imaging a hernia and working on uncooperative patients. The camera is presented as a neutral observer, allowing the viewer to feel they are getting a candid glimpse of the inner workings of a hospital. The film shows all aspects of the hospital from patients to resident training to waste disposal. It provides a unique view of a real hospital that feels refreshingly authentic. As Phases of Matter is more contemplative and artistic than informative, it would have little use in a classroom setting. There are some scenes that may be useful in health sciences courses, but overall, it is more for general audiences than academic ones. Though it is not recommended for academic settings, it is a beautiful and thought-provoking documentary general audiences will enjoy. For this reason, it is recommended with reservations. Trigger warning: Viewers should note that the documentary shows blood, medical procedures, preserved human specimens and other medical imagery that may be triggering or upsetting to some viewers. Awards:Best Documentary, 57th Antalya Golden Orange Film Festival 2020; Best Documentary, Istanbul Film Festival, 2020; Best Film Award, Best Director Award, Accessible Film Festival; Labocine Special Mention, 13th Imagine Science Film Festival
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.728 | 0.375 |
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".