Bibliographic record
Abstract
This interview is accompanied by Margherita R. Long's essay Japan's 3.11 Nuclear Disaster and the State of Exception: Notes on Kamanaka's Interview and Two Recent Films Born in Toyama Prefecture, Kamanaka Hitomi entered Waseda University and joined her friends in a filmmaking club. Kamanaka won a scholarship from the Japanese government and spent time in Canada and the US between 1990 and 1995 studying at the National Film Board of Canada and working as a media activist at Paper Tiger in New York. Kamanaka then returned to Japan at the time of the Hanshin-Awaji Earthquake that caused over 6,000 deaths and displaced over 300,000 people in the greater Kobe area of Japan in 1995. While working as a volunteer for the victims of the earthquake, she began to produce documentaries for NHK (Japan Broadcasting Corporation) as a freelance director. Kamanaka's first nuclear-related film, Hibakusha at the End of the World (Radiation: A Slow Death, 2003), won several awards, including one from Japan's Agency for Cultural Affairs for excellence in documentary. The film shed light on the transnational links of nuclear policies and their fatal consequences by comparing radiation effects at the Hanford Nuclear Reservation in the State of Washington, the effects of depleted uranium on Iraqi citizens during and after the first Gulf War, and victims of the atomic bomb in Hiroshima and Nagasaki.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".