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Record W2971399849 · doi:10.24043/isj.100

Blue therapeutic spaces on islands: Coastal landscapes and their impact on the health and wellbeing of people in Malta

2019· article· en· W2971399849 on OpenAlexvenueno aff
Bernadine Satariano

Bibliographic record

VenueIsland Studies Journal · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Aging, and Tourism Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyEnvironmental planningEnvironmental resource managementEnvironmental protectionEnvironmental science

Abstract

fetched live from OpenAlex

This paper emphasises that the coastal environment is important for the health and wellbeing of inhabitants living in deprived neighbourhoods in the small island state of Malta. Using qualitative research, it explores how the respondents experience their interaction with the coast and the sea in diverse ways and how this impacts on their health and wellbeing. Making use of qualitative in-depth interviews it analyses the symbolic connections that the respondents have with the sea, the potential that the natural, coastal environment has in enhancing physical activity and mental wellbeing, feelings of embodiment, social interaction and the aspect of temporality. Yet, some nostalgic memories also referred to the aspect of loss and the importance of protection of the natural coastline. This paper acknowledges the deep emotions and strong loving connections that Maltese inhabitants have with the coastal environment and how valuable these spaces are for their health and wellbeing. The fluid, dynamic landforms at sea are greatly important for the health and wellbeing of these individuals and are highly valued therapeutic landscapes within a densely built up environmental island context.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.302
Threshold uncertainty score0.961

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.018
GPT teacher head0.308
Teacher spread0.290 · 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

Citations26
Published2019
Admission routes1
Has abstractyes

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