This Atom Bomb in Me — with Lindsey Freeman
Bibliographic record
Abstract
From Mister Rogers to radioactive frogs, Below the Radar dives into the nuclear imaginary with SFU Associate Professor of Sociology Lindsey Freeman as she recounts the atomic culture she was brought up in. In this episode, Lindsey is in conversation with Am Johal about her new book, This Atom Bomb in Me, a reckoning with our nuclear past that resonates with the present moment. Through Lindsey’s experiences of growing up in the Manhattan Project secret city, Oak Ridge, Tennessee, the book traces the radiating influence of the arms race on American politics and culture. Lindsey also speaks to her current projects, including a trip to Chernobyl, the impact of rain on Vancouver’s social mood, and a fascination with miniatures and the uncannily small.\nLindsey A. Freeman is a writer and sociologist interested in atomic culture, feelings, memory, poetics, and rain. Freeman is author of This Atom Bomb in Me (Redwood Press/Stanford University Press) and Longing for the Bomb: Oak Ridge and Atomic Nostalgia (University of North Carolina Press), and editor of The Bohemian South: Creating Counter-cultures from Poe to Punk (University of North Carolina Press). Freeman is an Associate Professor of Sociology at Simon Fraser University and an Affiliated Researcher at the Espaces et Sociétés (Space and Society Center) at the University of Caen-Normandy.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 0.008 |
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".