Bibliographic record
Abstract
Discipline and Punish has been the seminal text for students of the rationality of disciplinary power. In recent years, critical scholarship has become increasingly keen to move analytically beyond the normative mode of disciplinary power. As such, D&P is increasingly marginalized as a text, in favour of Foucault’s later works. In this discursive context, this paper has a twofold aim. Firstly, I want to think through the transformations in labour control over the last 30 years of neoliberal counterrevolution in terms of the movement beyond disciplinary power. Secondly, I shall critique the autonomous and normative governmentality concept by the reinsertion of the ‘genealogy of capital’ in terms of the ontology of axiomatic capitalism. I shall address the undertreated genealogical movement from disciplinarity to governmentality, by arguing for something provisionally tagged meta-disciplinarity. The worth of such a move is to challenge the critical potency of the governmentality concept as is, in the belief that the ‘meta-disciplinary’ offers the most promising and relevant ligature from Foucault’s work into Marxist scholarship on the transformations of neoliberal capitalism and the technologies of its megamachine that confronts us 40 years on.
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.097 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| 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 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".