Human-Machinic Assemblages: Technologies, Bodies, and the Recuperation of Social Reproduction in the Crisis Era
Bibliographic record
Abstract
This dissertation argues that class composition, as defined and theorised by Operaismo and Autonomist thinkers, has had both a major and a minoritarian form. In fact class composition in its major form has always been subtended by a minor current. I examine both historical (the 1905 Russian Soviets, the 1919 Turin factory councils, the Italian social movements of the 1970s) and contemporary examples (the occupation of Tahrir Square in Egypt, the Indignados movement in Spain, and Occupy Wall Street in 2011, as well as the 2012 Quebec student strike) of class composition. From these examples I then argue that the minor current of class composition is rooted in social reproduction – both its crisis and its recuperation. And further that this minor current expands throughout history, growing to command greater attention within social and labour movements. Further, this dissertation argues that contemporary social movements appear today as an assemblage, a human-machinic assemblage, which enact social reproduction in crisis and recuperation through both embodied and technologized forms. I demonstrate the ways in which technologies of communication are implicated in forms of securitised and commodified social reproduction, but also open up new and powerful possibilities for autonomous and liberatory social reproduction. This dissertation relies on a merger of conceptual, theoretical, and field research and benefits from the author’s direct involvement in social and political struggles.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.043 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".