Retheorising capitalism
Bibliographic record
Abstract
The climate catastrophe and transgression of planetary boundaries, together with the erosion of democracy and rise of oligarchy, have intensified demands for critical reflection on capitalism. This edited collection responds to these demands, featuring contributions from scholars across the social sciences disciplines and geographical contexts. The book explores ways to rethink and retheorise capitalism through theoretical, conceptual, and empirical contributions. Some contributions propose ways to reform capitalism, some emphasise the need to examine it as part of diverse more-than-capitalist economic arrangements, while others invite us to reflect on what might come after capitalism. Embracing a pluralist approach, the book reflects the dynamism of capitalism and presents diverse theoretical approaches and methodologies. Retheorising, on the pages of this book, takes the form of reconceptualising, reimagining, representing, as well as repairing. From text-based analyses to visual collaging and pottery making, the chapters engage with capitalism in multifaceted ways and invite readers to also reflect on how we sense and experience socioeconomic formations through scholarly endeavours. Through its pluralist approach, the book urges readers to explore and trouble the multifaceted workings of capitalism and engage with the possibilities for its transformation or transgression.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| 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".