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Record W3024502877 · doi:10.24043/isj.115

Anywhere but here: Experiences of islandness in Pearl River Delta island tourism

2020· article· en· W3024502877 on OpenAlexvenueno aff
Zhikang Wang, Mia M. Bennett

Bibliographic record

VenueIsland Studies Journal · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicIsland Studies and Pacific Affairs
Canadian institutionsnot available
Fundersnot available
KeywordsPearlTourismGeographyDeltaFisheryArchaeologyEngineeringBiology

Abstract

fetched live from OpenAlex

This study considers the phenomenology of ‘islandness’ by analysing the experiences of tourists, islanders, and migrant tourism workers on two Chinese islands in the South China Sea. Although we begin by presuming place to be a phenomenological concept centring on ‘being-in-the-world’, we find that people’s experiences both on and off the islands of Dong’ao and Wailingding engender a desire to ‘be-in-many-worlds’ at once. Findings drawn from three months of ethnographic fieldwork suggest that while tourists privilege ‘being-at-the-seaside’, long-term residents prioritize being both ‘on’ and ‘off’ the island. Meanwhile, migrant tourism workers’ sense of islandness emerges from ‘being-at-theseaside’ and ‘being-on-the-island’. In all cases, we find that islands challenge people’s desires to dwell in just one specific place to which they have an attachment. We argue that this liminal place attachment arises partly because the physical geography of islands, being surrounded by the sea, facilitates movement and may prompt a longing for elsewhere. Our findings have consequences for the phenomenology of place, which assumes that people have an innate desire to be somewhere. Yet thinking through and from islands shows that people equally wish to be somewhere else, too. The manifold human experiences of islandness underscore the need for a more relational phenomenology of place based not just on ‘being-in-the-world’, but rather ‘in-many-worlds’ at once.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.065
Threshold uncertainty score0.728

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.044
GPT teacher head0.313
Teacher spread0.269 · 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 teacher head, 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

Citations12
Published2020
Admission routes1
Has abstractyes

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