From Red Scare to Red Scare, Grey Zone to Grey Zone
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
Abstract While social media platforms allow for depth of engagement and new opportunities for disruption, this chapter argues that State weaponization of domestic dissent is neither new nor novel. Russia has a long history of weaponizing dissent in the West, and the West has a long history of overestimating its extent and efficacy. Moreover, during the Cold War, the West also weaponized dissent, literally and figuratively, in grey zone activities in Eastern Europe. Our historical approach to this phenomenon analyses both the offensive and defensive actions of the Soviet Union and Canada and the United States. A major advantage of a historical perspective is that it highlights the very real harms done not only by the Soviet Union but also by our own over-reactions—in effect via the political, law enforcement, and judicial decisions that undermined the very democratic values that we were fighting to defend in the first place.
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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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