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Record W4210798445 · doi:10.33137/cq.v6i2.36946

Democratic Socialism a Solution to Colonial Tourism Structures

2022· article· en· W4210798445 on OpenAlexaffvenue
Kennedy-Jude Providence

Bibliographic record

VenueCaribbean Quilt · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTourismContext (archaeology)DemocracyDevelopment economicsSocialismPolitical economyPolitical scienceColonialismEnvironmental degradationEconomyGeographyEconomic growthSociologyEconomicsLawCommunism

Abstract

fetched live from OpenAlex

The Caribbean’s environmental diversity and tourism products have been a longstanding source of income leading many to argue that the detriments of tourism outweigh its beneficial, economic effects. However, as the COVID-19 pandemic changed the course of travel- and by extension, tourism, countries have been forced to re-evaluate travel structures, means of income and longstanding Clientelist relationships with their North American neighbours. In this commentary, I discuss the socio- economic effect of the COVID-19 pandemic on tourism- in the context of Jamaica; as well as the possibility of re-engineering Democratic Socialism for implementation in the post-pandemic environ- ment as a way to psychologically decolonize the region and alleviate the potentially lingering, devastating effects of the pandemic. Furthermore, while there are other prevalent issues that threaten tourism and have plagued the region for years including and not limited to pollution, environmen- tal degradation, climate change, crime and natural disasters, this analysis is simply intended to focus on identity, economy and the seemingly never-end- ing cycle of Western Imperialism threatening West Indian identity.

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)0.005
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0120.046
Scholarly communication0.0070.005
Open science0.0010.007
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0040.000

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 head0.031
GPT teacher head0.336
Teacher spread0.305 · 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 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
Published2022
Admission routes2
Has abstractyes

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