Spatial Alternatives and Counter-Sovereignties in Israel/Palestine
Bibliographic record
Abstract
The securitization of the spaces of Israeli-Palestinian interaction, from checkpoints to the West Bank Separation Wall, continues to intensify and receive attention from journalists, scholars, and activists. Understandably, the focus is on the negative consequences of existing spatial configurations. Receiving far less attention is the development of alternative spatial formations which might advance forms of “desecuritization,” especially in those spaces that are crucial hinges of Israeli-Palestinian interaction (Jerusalem and other mixed cities, the Wall, the Green Line, roads). This article explores whether alternative ways of using, organizing, experiencing, and coexisting in space—especially at the micro level—hold out promise for helping to reframe significant dimensions of Israeli-Palestinian interaction. It seeks to better understand whether disjointed forms of sovereignty that appear—or disappear—across the occupation can be met by counter-sovereignties; whether new spatial counter-realities can be articulated through everyday life; and whether forms of agency, especially contestation, can reset understandings of, and perspectives on, spaces. A range of examples are considered within Jerusalem, mixed cities, the occupied Palestinian territories and at the border, bearing on religious sites, healthcare, gentrification, security infrastructure, popular protest, and festivals.
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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 | 0.001 |
| Science and technology studies | 0.007 | 0.039 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".