Learning to Glow: A Nuclear Reader
Bibliographic record
Abstract
Sonya Huber is a contributing author, "All in the Family." Book description: Atomic energy is not only invisible, it has been cloaked in secrecy by government, industry, and the military. Yet for many Americans the effects of radiation have been less than secret. Just ask the radium workers in Ottawa, Illinois, the "downwinders" of Utah, or unsuspecting veterans of the Gulf War. When told from the perspective of ordinary people, nuclear history takes on a much different tone from that of the tranquil voices of authority who always told us we had nothing to fear. In Learning to Glow, twenty-four essays testify to many of the unsuspected human and environmental costs of atomic science. They show that Americans have paid a terrible price for supposedly "winning" the Cold War--for although the nuclear nightmare may be over, we are still living with nuclear threats every day. Writers such as Scott Russell Sanders, Terry Tempest Williams, and Barbara Kingsolver reveal the psychic and emotional fallout of the Cold War and of subsequent developments in nuclear science. The essays include personal testimonies of what it was like to grow up with family members in nuclear-related jobs; hard-hitting journalism on the health and environmental costs of our nuclear policies and practices; and poignant stories of coming to terms with nuclear power, including contributions by writers who revisit Hiroshima in an attempt to heal the wounds left by the Bomb. These essays offer an alternative to the official version of nuclear history as told to us by school textbooks, government authorities, and nuclear industry officials. They are stories of and by ordinary people who have suffered the consequences of the decisions made by those in power-stories that have been largely ignored, dismissed, or suppressed. They will challenge readers to re-examine their preconceptions about the way we deal with issues of nuclear arms and radioactive waste because they show that nuclear history does not belong to experts but to us all.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.048 | 0.030 |
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; both teacher heads agree on what is shown here.
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".