Everyday secrecy: Oral history and the social life of a top-secret weapons research establishment during the Cold War
Bibliographic record
Abstract
Abstract Despite the welcome turn within security studies towards a more material- and practice-oriented understanding of state secrecy, the ways in which security actors experience, practise and negotiate secrecy in their everyday work lives has been rather overlooked. To counter this neglect the article calls for attention to everyday secrecy. Focusing on a former top-secret weapons research facility in the UK called Orford Ness, it uses oral history to give an account of ex-employees’ memories, experiences and practices concerning secrecy. Such a focus reveals that subjects make sense of procedures and rules of secrecy in ways that are sometimes surprising and unexpected. Ultimately this perspective emphasizes that secrecy is not just what governments and organizations prescribe and proscribe; it is also shaped by subjects who negotiate these rules. Everyday secrecy matters: as a perspective it shows that secrecy is not simply imposed by states and organizations from ‘above’; it is also made from ‘below’, albeit very asymmetrically.
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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.025 | 0.051 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".