Bibliographic record
Abstract
Neoliberalism is easily one of the most powerful concepts to emerge within the social sciences in the last two decades, and the number of scholars who write about this dynamic and unfolding process of socio-spatial transformation is astonishing. Even more surprising though is that there has, until now, not been an attempt to provide a wide-ranging volume that engages with the multiple registers in which neoliberalism has evolved.The Handbook of Neoliberalism seeks to offer a wide-ranging overview of the phenomenon of neoliberalism by examining a number of ways that it has been theorized, promoted, critiqued, and put into practice in a variety of geographical locations and institutional frameworks. With contributions from over 50 leadingauthors working at institutions around the world, the volume’s seven sections provide a systematic overview of neoliberalism’s origins, political implications, social tensions, knowledge productions, spaces, natures and environments, and aftermaths in addressing ongoing and emerging debates. The volume aims to provide the first comprehensive overview of the field and to advance the established and emergent debates in a field that has grown exponentially over the past two decades, coinciding with the meteoric rise of neoliberalism as a hegemonic ideology, state form, policy and program, and governmentality. It includes a substantive introductory chapter and will serve as an invaluable resource for undergraduates, graduate students, and professional scholars alike
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
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".