Bibliographic record
Abstract
A good deal of the impetus for writing this book is connected to the conversations that happened between myself and many others who are often connected to the scholarly meetings that I attended between 2011 and 2018.Many of these conversations, especially in the case of people affiliated with cultural studies, were based on reimagining, rethinking, and disrupting the link between culture and discourse (or rhetoric and communication) when thinking about resistance.My intervention is to think about the connection between culture and protest tactics.Although none of the people mentioned in these acknowledgments bear any responsibility for the limitations of this book, I owe a debt of gratitude, in some cases deep, to the following people and organizations for the following reasons.To begin, fragments of Culture and Tactics were presented at several academic meetings.Principle among these was the Union for Democratic Communications (UDC).My work found its home here and also with some of the Gramsciani from the United States, Canada, the United Kingdom, and Brazil who organized and participated in panels and special seminars at the National Communication Association (NCA), the Cultural Studies Association (CSA), Rethinking Marxism, and the American Association of Geographers Socialist and Critical Geography Knowledge Community or Socialist Geographers Circle, and the International Social Theory Consortium.At the NCA and in the Union for Democratic Communications, I wish to thank the following people.First, I want to thank the organizers of Revolutionary Voices: Marxism,
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.282 | 0.220 |
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".