Transcending Nationalist Divides: Religious Reconciliation as the Basis for a One‐State Solution in Israel/Palestine
Bibliographic record
Abstract
Abstract Nationalism's inability to yield peacefully coexisting forms of political identity in Israel/Palestine has persisted for more than a century. This is so whether one refers to strands of secular nationalism that composed predominating, modern historical foundations for Israeli and Palestinian political consciousness, or subsequent forms of nationalism that have become intertwined, ever more, with religion. Further, nationalism's failure to foster a way out of the Israel/Palestine impasse infects not only the familiar (but increasingly problematic) “two‐state” solution but also the contested (but perhaps more productive) “one‐state” solution. The one‐state solution has tended to involve a secular approach, for example, the binational variety emblematized by Edward Said, or, alternatively, a nonbinary democratic state where equal citizenship is not contingent on distinct forms of identity. However, the untapped promise of the one‐state solution could be better actualized with ingredients for the construction of citizenship that, in a real, spiritual sense, transcend the limiting divisions of nationalism. Specifically, shared religious roots, including the modes of reconciliation integral to the three Abrahamic traditions—Judaism, Christianity, and Islam—most directly ensnared within the Israel/Palestine bind might offer a more fruitful basis for coexistence.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.031 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".