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Record W3009139118 · doi:10.1344/co20192772-89

Under the Mermaid Flag: Achzivland and the performance of micronationality on ancestral Palestinian land.

2019· article· en· W3009139118 on OpenAlexaboutno aff
Philip Hayward

Bibliographic record

VenueCoolabah · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicJewish and Middle Eastern Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFlag (linear algebra)RhetoricLiteral and figurative languagePoliticsSymbol (formal)Character (mathematics)Interpretation (philosophy)FantasyHistoryLiteraturePopulationThe SymbolicSociologyArtLinguisticsPhilosophyLawPolitical scienceDemography

Abstract

fetched live from OpenAlex

This article considers the relationship between symbolism, interpretation and grounded reality with regard to “Achzivland,” a small area on the eastern coast of the Mediterranean that was declared an independent micronation in 1972. The article commences by identifying the principal geo-political and military factors that created the terrain for the enactment of fantasy utopianism, namely the forced removal of the area’s Palestinian population in 1948 and the nature of Israeli occupation and management of the region since. Following this, the article shifts to address related symbolic/allusive elements, including the manner in which a flag featuring a mermaid has served as the symbol for a quasi-national territory whose founder — Eli Avivi — has been compared to the fictional character Peter Pan, and his fiefdom to J.M. Barrie’s fictional “Never Never Land”. Consideration of the interconnection of these (forceful and figurative) elements allows the discourse and rhetoric of Achzivland’s micronationality to be contextualised in terms of more concrete political struggles in the region.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.013
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.026
GPT teacher head0.277
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2019
Admission routes1
Has abstractyes

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