Protective abandonment: Risk, data, and surveillance of nuclear workers post Fukushima
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it