Bibliographic record
Abstract
Distributed by Grasshopper Film, 12 East 32nd St., 4th Floor, New York, NY 10016Produced by Boris GaraviniDirected by Ekiem Barbier, Guilhem Causse, and Quentin L'Helgouac'h2023, Streaming, 96 mins Knit’s Island is a fascinating, genre-bending film that explores video game subculture in the multi-player, survivalist game, DayZ. The feature length documentary is shot completely inside the game and follows a team of filmmakers as they encounter players and prompt them to reflect on their experiences, behavior, and identity in and outside of the game. The players assume a wide variety of roles from one extreme of human behavior to another. Inside DayZ the players, many of which have played for years, commit violent acts, plant gardens, dance at raves, go on hikes, and form real bonds with one another. The filmmakers ask them to consider what the game means to them and how they differentiate it from their everyday lives. For most, the game offers an escape and allows them to experiment with behavior and identities they would not otherwise do or take on. The film is highly creative and includes many beautiful, albeit digital, cinematic shots. This film is highly recommended for those interested in exploring video game play and subcultures. Awards:2023 Winner, Special Prize, Yamagata International Documentary Film Festival; Montreal International Film Festival, Best International Feature; Camden International Film Festival, Emerging Cinematic Vision Award
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.643 | 0.214 |
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".