Bibliographic record
Abstract
Opponents of states and capital must be prepared to defend ourselves. To understand the nature of the state is to know that it will attack to kill when and where it feels a threat to its authority and power. But the struggles against exploitation, oppression, and repression must also move to the offensive. With the emboldening of reactionary forces on the far Right, there has been a renewed focus on issues of community self-defense, not only against the violence of the state but against organized fascists and Right-wing vigilantes alike. There has also been a developing seriousness, particularly among anarchist and antifascist, or antifa, activists. The goal of all anarchism is not to eliminate violence in social struggle (a futile and impossible pursuit given the nature of the state), but to limit the amount, degree, and extent of violence and harm inflicted by state agents, and their vigilante supporters, on the poor, oppressed, and exploited. And this is part of the emphasis on insurrectionary infrastructures. Non-material (emotional) and material resources and spaces are necessary to defend communities and workplaces under attack, but also to organize possible, and necessary, offensives. Insurrectionary Infrastructures reflects on strategies and tactics of rebellion and resistance and offers suggestions for fighting to win
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.063 | 0.020 |
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".