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Record W3174283013 · doi:10.24043/isj.163

'Splendid isolation': Embracing islandness in a global pandemic

2021· article· en· W3174283013 on OpenAlexvenueno aff
Karl Agius, Francesco Sindico, Giulia Sajeva, Godfrey Baldacchino

Bibliographic record

VenueIsland Studies Journal · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicIsland Studies and Pacific Affairs
Canadian institutionsnot available
FundersEuropean Commission
KeywordsTourismPandemicDestinationsCoronavirus disease 2019 (COVID-19)AccommodationGeographyIsolation (microbiology)BusinessDevelopment economicsEconomyEconomicsMedicine

Abstract

fetched live from OpenAlex

Islandness is often considered to be a disadvantage. However, it has helped the residents of islands to delay, deter, and, in some cases, totally insulate themselves from COVID-19. While islanders have been quick to lock themselves down, this has had a tremendous impact on their connectivity and on tourism, which in many cases is their major economic sector. Yet, the association of islands with being safe, “COVID-19 free” zones has helped these spaces to be among the first destinations to restart the tourism economy once travel restrictions were lifted. After several weeks of lockdown, and with the COVID-19 threat still looming, social distancing remained the norm. Travellers were thus eager to immerse themselves in island environments while avoiding crowds and seeking small accommodation facilities in less densely populated rural areas to limit the risks of infection — a package offered by several islands in the central Mediterranean. With many travellers opting to travel close to home, islands benefited from domestic tourism — a key market segment for islands in this region. Islands have thus performed relatively well in fighting the COVID-19 pandemic and in restarting their economies; but the pandemic has also exposed challenges including a dangerous overreliance on tourism.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.714
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.047
GPT teacher head0.368
Teacher spread0.320 · 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.

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

Citations17
Published2021
Admission routes1
Has abstractyes

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