Bibliographic record
Abstract
Post-structuralist and feminist scholars gained new insights into bodies as ‘sites of contestation’ by exposing power’s influence on them – it is this nexus that is captured in Silvia Federici’s exhaustive analysis centering on the mysterious figure of the ‘witch’ and firmly pointing to the power of the profit-motivated, capitalist system, ever patriarchal in its repressive control of women (and their bodies), nature, and the wider society. Her work on bodies and capital emerged from the cross-fertilization of Marxist and feminist geographies that shaped the autonomist epistemology and intellectual strengths that have contested the masculinist denial of sexed, classed, racialized, illegalized, homophobic, disabled bodies and their relation to power. In the manuscript, I elaborate Federici’s analysis of women’s bodies and capital within feminist bodily geographies but also show the key lacks in leftist and radical reactions on issues around women’s oppression. Following Federici’s imperatives of capitalism, patriarchy and primitive accumulation in Europe and drawing from my field experiences in India and elsewhere, I argue that the global diffusion of capitalism to non-capitalist regions pointing towards the variations in the forms of accumulation in different places. Caliban and the Witch transcends to many feminisms.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.055 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".