Bibliographic record
Abstract
In 2019, the hosts of UNDOX picked their shortlists for the 3rd International Festival of Innovative Documentary Art. Tsveta Dobreva (FID Marseille), Andreas Ungerböck (ray Filmmagazin), Claudia Slanar (Belvedere 21), anthropologist Minou Norouzi (Goldsmiths University of London and Helsinki Collegium for Advanced Studies) and the Viennese journalist Alejandro Bachmann. All of them presented their favourite films personally at UNDOX 2019 – alongside with the filmmakers Elise Rasmussen (New York), Ute Adamczewski (Berlin), Ralo Mayer (Vienna), Jean-Marc Chapoulie and Randa Maroufi (both from Paris). \n \nOn Saturday 23 November 2019 Minou Norouzi hosted screenings of: \n \nDID YOU KNOW BLUE HAD NO NAME? by Elise Rasmussen - USA/Canada/Switzerland/France 2018, 7 Minutes \n \nSEMRA ERTAN by Cana Bilir-Meier - Turkey/Germany 2013, 7 Minutes \n \nACROPOLIS by Eva Stefani - Greece 2004, 25 Minutes
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.425 | 0.091 |
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".