Vague and unworkable: The fuzziness of the archipelago as a concept and its unsuitability as model for a 21st century Palestinian nation
Bibliographic record
Abstract
While it is frequently invoked, the archipelago is such a vague concept that its deployment in fields such as island studies is only productive when the contingency of its use is specified. In this article, we examine the concept itself and then consider the use of the archipelago as a metaphor and/or model for a future Palestinian state. The creation of the modern nation-state of Israel in Palestine in 1948, various Israeli military actions, and (often related) public and private developments of former Palestinian lands has resulted in a substantial proportion of Palestinians fleeing to neighbouring countries (chiefly Jordan, Lebanon, and Syria). Those Palestinians who have remained have largely been confined to territorial isolates within the Jewish state. These isolates have frequently been understood and analogised as ‘islands’ within Israel, and the aggregation of these isolates has been variously referred to and/or represented as an archipelago. This article examines the development of this metaphoric interpretation of the Palestinian community within Israel in Anglophone, Arabic, and Francophone discourse, and characterises the contortions necessary to imagine Palestinian territories as archipelagic. The conclusion returns to consideration of the notion of the archipelago itself and of its usefulness in island studies and other contexts.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.060 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".