Bibliographic record
Abstract
Legal regimes dealing with security have prominent temporal attributes: they are often intended to operate for a specified period of time in response to an imminent danger and allow governments to employ extraordinary measures enabling them to act faster when faced with time- critical scenarios. Such regimes also have prominent spatial attributes: they often protect a physical border, delineate spaces with extra-legal status, or use surveillance measures such as cameras or patrols for monitoring designated areas. Based on the analysis of one security regime that was imposed on the Palestinian minority in Israel between 1948 and 1966, I argue in this article that such spatial and temporal attributes affect each other and that different legal measures associated with security can be characterized by the specific time/space configuration – or chronotope – embedded in them. In Israel, a military regime was imposed on specific areas to foil the movement of its subjects and thereby further its territorial objectives. The procedures that the regime employed were therefore designed to monitor and control individuals’ whereabouts. Each of these seemingly spatial attributes, however, gained its particular function and meaning from a specific interaction with temporal attributes. The prism of the chronotope illuminates how security legal measures generate distinct modes of governance and produce different boundaries to the political community, in a way that a one-dimensional analysis fails to capture.
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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 | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".