Israel-Palestine and the Settler Colonial ‘Turn’: From Interpretation to Decolonization
Bibliographic record
Abstract
In recent years there has been a powerful resurgence of settler colonialism as an interpretive framework through which to understand the Israeli-Palestinian conflict. Attached to the burgeoning field of settler colonial studies, this so-called ‘turn’ to settler colonialism has seen Israel-Palestine increasingly compared alongside New World white settler societies like Australia, Canada and the United States. In seeking to undercut the lens of exceptionalism through which the conflict has conventionally been viewed, the settler colonial paradigm has some important counter-hegemonic implications for reframing Israel-Palestine, not least of which is its prescription for decolonization. However, it is paradoxically in the context of decolonization that the limits of the settler colonial paradigm become most apparent. I argue that these limitations are connected to the dominance of Patrick Wolfe’s structural account of settler colonialism, which leaves very little room for transformation, and to the particular connotations settler colonial studies has acquired from the New World contexts in which it is most often articulated. This is particularly the case in Israel-Palestine, where these connotations preclude engagement with the national aspects of the conflict and leave under-examined the unique resonances of the settler/native distinction, which need reckoning with in any serious account of decolonization.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.068 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| 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".