Protective abandonment: Risk, data, and surveillance of nuclear workers post Fukushima
Bibliographic record
Abstract
During the coronavirus disease-19 pandemic, Fukushima marked the 10th anniversary of its nuclear disaster of 2011. And although pandemic scientists around the world used technological surveillance to predict risks, the experiences from the Fukushima health crisis call into question such technological solutionism. The Japanese government and electronic companies had placed nuclear workers under intensive health surveillance for decades, but the health data rarely helped workers to protect themselves. Rather, the government has often used the data to decline workers’ claims for medical compensation. I call this contradictory consequence of data Protective Abandonment, the systematic disposal of people through the promise of protection. Data are collected through surveillance, for the purpose of risk management, but the information ends up protecting only the existing political economic systems. Crucially, data collection disguises protection and hides the unequal distribution of care. I argue that protective abandonment may become a common experience in today’s data-driven societies.
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 | 0.014 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.021 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".