Impersonal subjectivation from platforms to infrastructures
Bibliographic record
Abstract
The rapid expansion of social media has led to the concentration of digitized, networked, and mediated processes into the hands of a few giant corporations (e.g. Google, Facebook, and Amazon), their partners and affiliates. From smart watches to targeted advertising and reputation scores, this new political economy of subjectivation – or subject making – sees an intensification of datafication to sell commodities, manipulate moods, inject ideologies, and influence behaviors. This article argues that in order to understand this new political economy of subjectivation, we need to complicate and build upon framework that focus on the collection of personal data and its risks on individual users. We argue that as social media and digital media giant corporations move away from an enclosed platform model toward a distributed, impersonal infrastructure, the mining of individual data and the shaping of individual attitudes is increasingly geared toward establishing relationships between user data and a plethora of non-human, environmental data. Such an infrastructure invokes impersonal subjects, and thus requires a new politics of relationality.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.040 |
| Scholarly communication | 0.020 | 0.028 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".