Bibliographic record
Abstract
The creative commons documentary Preempting Dissent (2014) builds upon the book of the same name written by Greg Elmer and Andy Opel. The film is a culmination of a collaborative process of soliciting, collecting and editing video, still images, and creative commons music files from people around the world. Preempting Dissent interrogates the expansion of the so-called “Miami-Model” of protest policing, a set of strategies developed in the wake of 9/11 to preempt forms of mass protest at major events in the US and worldwide. The film tracks the development of the Miami model after the WTO protests in Seattle 1999, through the post-9/11 years, FTAA & G8/20 summits, and most recently the Occupy Wall St movements. The film exposes the political, social, and economic roots of preemptive forms of protest policing and their manifestations in spatial tactics, the deployment of so-called ‘less-lethal’ weapons, and surveillance regimes. The film notes however that new social movements have themselves begun to adopt preemptive tactics so as not to fall into the trap set for them by police agencies worldwide.
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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.080 | 0.023 |
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".