Colonial Racial Capitalism and Violence: Theorising the Relationship between Empire and Israeli Settler Colonialism
Bibliographic record
Abstract
This article theorises the relationship between empire and Israeli settler colonialism through the interplay between violence and capital accumulation in colonial racial capitalism. It argues that Israel's genocidal violences of land theft is the means and grounds for Israeli and US imperial capital accumulation. This can be observed in the genocidal operation in the Gaza Strip. The first section contextualizes the genocide in the Gaza Strip within a larger structure of genocide. The section highlights the centrality of the conquest of land in the Zionist settler colonial project and how that appealed to the British empire in the early parts of the twentieth century. The second section examines why the US empire has actively participated in the genocide. States in the US imperial bloc essentially reduce the whole question of Palestine to a simple equation where Palestinian liberation does not equate with their own economic interests, while Israeli settler colonialism does. Though there exists a tension between imperial and settler colonial interests around the question of what kind of violence is preferable for capital accumulation, the article concludes by arguing that a transformation of the logics and systems of colonial racial capitalism cannot arise without activism and pressure from below on states and capitalist structures.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.038 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".