Impersonal subjectivation from platforms to infrastructures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it