2 Big Data, governmentality and social acceleration: the industrialization of politico-institutional mediation
Bibliographic record
Abstract
This proposal builds on Rosa's work on social acceleration (Rosa, 2012). It examines the deployment of technologies that have brought about societal changes, particularly those having to do with social (re)production. We posit that the concept of social acceleration plays a major role in the Big Data phenomenon, more specifically Big Data as industrializing dynamic of politico-institutional mediation. To support this assertion, we draw on the work of Freitag (1986), which homes in on the symbolic dimension of social issues and conceives of society essentially as a (re)production dynamic that is individuated through time. From this perspective, one of the main characteristics of society is its strict reproduction arrangements, which require a comprehensive synthesis (representation) of the social realm. Once transcendental in nature (a reproduction mode based on symbolic politico-institutional mediation), this synthesis now tends to appear in decision-making/operational reproduction practices. There society abandons the yet to mode to take explicit, and absolute, present-tense form as systemic technical efficiency. Big Data epitomizes this intensifying trend through its deployment of industrial-grade tools for automating symbolic politico-institutional mediation processes and promoting their incorporation into marketing channels as productive activities in their own right (Manard, Mondoux et al., 2016). This so-called real-time dynamic itself, in requiring that acceleration be perpetual, indeed proves problematic given that symbolic politico-institutional mediation is no longer a political matter (Mouffe 2005). Instead, mediation is subsumed by technical devices engendering normative processes that assume apolitical and non-ideological guises.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.035 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".