The politics of algorithmic management class: composition and everyday struggle in distribution work
Bibliographic record
Abstract
This research enquires into the politics of organization, control and resistance in distribution workplaces. Situated within an autonomist Marxist conceptual framework, I make a case for the restoration of the spirit of the workers inquiry to class composition analyses of contemporary workplaces, particularly regarding the strategic need to understand the politics of algorithmic management. Although largely lost since the ‘post-autonomist’ turn, I argue the ‘interested’ methodological approach of the workers inquiry as developed within operaismo is especially pertinent to understanding contemporary class struggle within algorithmically-mediated workplaces. \n \nI highlight the political deficit in initial studies of the emergence of algorithmic management through engagement with a genealogy of scientific, cybernetic and humanistic management approaches. In doing so, I excavate the class politics of knowledge and communication, which remain prevalent in softwarized managerial forms. Combining an interdisciplinary theoretical basis with original empirical engagement, the inquiry builds an understanding of the technical composition of a number of distribution workplaces, detailing the managerial and working processes and highlighting the role of tracking, metrics and communication. \n \nDevices such as handheld radio data terminals provide the research with a space for thinking about the politics of algorithmic management because they mediate informational asymmetry between workers and managers, which I examine through consideration of such effects as ‘managerial distantiation’ and the uncertain place of supervisors within the algorithmic management infrastructure. \n \nI argue that workers are politically active in distribution workplaces, often aside from trade union involvement, and that there exists an infrapolitical realm where workers take advantage of the technologically reshaped terrain of struggle. These subversive actions, I argue, are characterised by metis (cunning intelligence), which challenges the forms of political action typically found in the workplace organizing repertoire by providing an alternative basis of commonality and collectivity based on the use of guile despite initially adverse conditions.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.039 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".