Bibliographic record
Abstract
56th BERLINALE 2006 With 160,000 tickets sold and an overall audience of circa 400,000, the 56th Berlinale (9-19 February 2006), the fifth under the aegis of Dieter Kosslick, will go down as the biggest, if not the best, in its festival history. Add to this the super success of the European Film Market (EFM) in the spacious Martin-Gropius-Bau - where 5,162 accredited participants representing 250 companies from 51 countries promoted over 650 films with 1100 screenings - and you have perhaps the largest turnout ever recorded at a film market. Of course, the rescheduling of the American Film Market (AFM) from spring to autumn had something to do with the big numbers at the EFM in Berlin. The only bumps in the festival road are still the unpredictable winter weather (not too uncomfortable this year, however) and the five-minute walk (with your winter coat on) from the Berlinale headquarters...
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.630 | 0.459 |
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".