Bibliographic record
Abstract
Over the last three decades we have witnessed the birth of a subject that has constituted the foundations of a regime change in state societies: the neoliberal subject. As much as neoliberalism came to mean the withdrawal of the state from certain arenas, the decline of social citizenship, privatization, downloading, and so forth, it also meant, if not predicated upon, the production of an image of the subject as sufficient, calculating, responsible, autonomous, and unencumbered. While the latter point has been a topic of debate concerning the rational subject, I wish to argue that the rational subject has itself been predicated upon and accompanied by another subject: the neurotic subject. More recently, it is this neurotic subject that has become the object of various governmental projects whose conduct is based not merely on calculating rationalities but also arises from and responds to fears, anxieties and insecurities, which I consider as ‘governing through neurosis’. The rise of the neurotic citizen signals a new type of politics (neuropolitics) and power (neuropower). I suggest a new concept, neuroliberalism—a rationality of government that takes its subject as the neurotic citizen—as an object of analysis.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".