Bibliographic record
Abstract
Since its spring 2013 North American release Margarethe von Trotta's film Hannah Arendt has garnered almost universal praise for its “extraordinary depth”; its“mesmerizing” and “suspenseful portrait” of a deep and daring thinker; its especially compelling and successful effort to make ideas entertaining as well as “sexy”(Sheri Linden, “A Persuasive Force to ‘Hannah Arendt,’” Los Angeles Times, June 7, 2013; Marsha McCreadie, “Hannah Arendt Brought to Life by a Mesmerizing Barbara Sukowa,” Village Voice, May 31, 2013; Marc Mohan, “‘Hannah Arendt’ Review: Talking, Typing, Smoking,” Oregonian, June 28, 2013; Deborah Young, “Hannah Arendt: Toronto Review,” Hollywood Reporter, Sept. 9, 2012). Even those film critics unwilling to go so far as “sexy”—those who find the dialogue stilted or the depiction of mid-twentieth-century New York literary life overly theatrical—generously acknowledge the monumental task von Trotta set for herself: showing audiences what the work of “thinking,” that all-important practice for Hannah Arendt, looked...
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.052 | 0.015 |
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".