Bibliographic record
Abstract
Michael Surbaugh presents a compelling and articulate account of a troubling dimension of neoliberalism.In debunking naive presuppositions in special education, Surbaugh has demonstrated the important contributions that philosophy of education can offer to an analysis of current educational issues.In response to a possible charge that he is over-intellectualizing a simple problem and a potentially worthwhile solution, Surbaugh demonstrates how a facile acceptance of seemingly straightforward presuppositions would allow ever greater and more subtle strategies by which the subject is disciplined into market relations.Indeed, this is the basic thrust of Michel Foucault's work: how disciplinary projects and strategies of governmentality are more readily internalized and taken up by the subject when rendered benign and construed as benevolent.We can miss the hidden danger when no overt or physical forms of violence are required to keep a population in check, such that self-determination masks social control, and self-maintenance becomes self-surveillance.For the most part, I am sympathetic with Surbaugh's aims and concerns.In this essay, I explore the notion of independence and interdependence as they relate to neoliberalism and education, beginning by describing how neoliberalism is itself impossible without interdependence.I then raise a meta-theorical issue by examining how philosophical work is conducted with respect to Foucault and John Dewey.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.497 | 0.199 |
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".