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Record W4413296296 · doi:10.63332/joph.v5i12.3223

The Rise of Posthumanism in Marine Ecotourism: A Comprehensive Bibliometric Analysis

2025· article· en· W4413296296 on OpenAlexaboutno aff
Donny Juliandri Prihadi, Ghulam Murtaza Lahbar

Bibliographic record

VenueJournal of Posthumanism · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsnot available
Fundersnot available
KeywordsPosthumanismEcotourismGeographyData scienceFisheryEnvironmental ethicsBiologyComputer scienceArtAestheticsPhilosophyTourismArchaeology

Abstract

fetched live from OpenAlex

This study explores the intellectual landscape of posthumanism in the context of ecotourism and sustainability through a bibliometric and keyword co-occurrence analysis using VOSviewer. By mapping the collaboration networks of authors and countries, and visualizing the co-occurrence of key terms, the research identifies the most influential contributors, emerging thematic clusters, and interdisciplinary linkages within the field. The study reveals that authors from the United Kingdom, Canada, and Australia are central to scholarly collaborations, while thematic clusters show an integration of critical theory, environmental justice, and sustainable development. Keyword analysis highlights the growing traction of philosophical and ecological terms that emphasize more-than-human perspectives in tourism research. These findings underscore the importance of rethinking traditional tourism paradigms and advocate for a more inclusive, relational, and sustainable approach to ecotourism grounded in posthumanist ethics.

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)0.015
metaresearch head score (Gemma)0.061
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.831
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.061
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.1690.232
Science and technology studies0.0020.002
Scholarly communication0.0080.005
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.247
Teacher spread0.240 · 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.

Study designNot applicable
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

Citations0
Published2025
Admission routes1
Has abstractyes

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