Bibliographic record
Abstract
Abstract The pervasiveness of neoliberalism within the field of human geography is remarkable, especially when we consider its virtual absence from the literature less than a decade ago. While the growing attention afforded to neoliberalism among geographers is new, the phenomenon of neoliberalism is not. This paper traces the intellectual history of neoliberalism and its expansions across various institutional frameworks and geographical settings. I review the primary contributions geographers have made to the literature, and specifically their recognition for neoliberalism’s variegations within existing political economic matrixes and institutional frameworks. Contra the prevailing view of neoliberalism as a pure and static end‐state, geographical inquiry illuminates neoliberalism as a dynamic and unfolding process. The concept of ‘neoliberalization’ is thus seen as more appropriate to geographical theorizations insofar as it recognizes neoliberalism’s hybridized and mutated forms as it travels around our world. I also consider some of the most salient ways that neoliberalism has been theorized among human geographers. In particular, I highlight understandings of neoliberalism as a hegemonic ideology, as a policy‐based approach to state reform, and as a particular logic of governmentality, arguing that while there are significant differences between these various formations, it may also be important to work beyond methodological, epistemological, and ontological divides in the larger interest of social justice.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.061 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".