Bibliographic record
Abstract
Because this book began in the same project as Cold War Space and Culture in 1960s and 1980s America, gratitude and debts frequently overlap.So a lot is repeated from that book's Acknowledgments.We start with the infrastructure.I am grateful to the American University College of Arts & Sciences Dean's Office, and especially former Dean Peter Starr, for many years' support of my travel and research, and for underwriting the cost of images and permissions when fair use did not apply.The American University Library Interlibrary Loan office must at times have thought from my requests that I was cracked (The Coming of the Rats?… The Survivalist #8: The End Is Coming?) but never blinked in procuring hard-to-find pulp for my scholarly consumption.The New York Public Library's generous MaRLI program granted me invaluable access to the circulating libraries and research spaces of the NYPL Main Branch, New York University, and Columbia University.The staff of the NYU library, where I most frequently exercised my MaRLI privileges, was unfailingly helpful.And during a sabbatical year, the now-retired Jay Barksdale granted me access to the charmed confines of the NYPL's Wertheim Study, my principal opportunity for full-time concentration during the many years of work on this project.Over the years, Chris Lewis (now retired), Sean Casey, and the rest of the team at the AU Library's Media Services have been essential to this project: buying or borrowing media as needed, and Sean in particular with key assistance in pulling together the final selection of frame grabs.Thanks to Princess Pratt, Cheyenne Dawson, and the rest of the team at Alamy; Nickie Osborne
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.627 | 0.423 |
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".