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Record W1499642463 · doi:10.1111/cag.12008

Tourists as colonizers in Quintana Roo, Mexico

2013· article· en· W1499642463 on OpenAlexaffvenue
Denise Fay Brown

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAppropriationColonialismTourismYucatan peninsulaEthnologyGeographyPoliticsContext (archaeology)PeninsulaColonizationState (computer science)HistoryEconomyPolitical scienceArchaeologyEcologyLaw

Abstract

fetched live from OpenAlex

Spatial appropriation is an age‐old strategy for domination by one group over another. In the context of national states, territorial expansion is a common manifestation of this. Spain's colonization process began in the Yucatan peninsula in Mexico in the sixteenth century but remained incomplete in this area. Independent Mexico's struggle for control over the Mayan landscape of the Yucatan continued through the nineteenth and twentieth centuries. It is my contention that the assault has continued in recent times. Today, it is not the conventional notion of nation state colonialism but a much more subtle invasion brought about by the ability of tourists from richer nations to travel south. Using the paradigm of settler colonization, this article proposes that relationships of power underlying this new infiltration parallel those of conventional colonialism, and that the tourist is, in fact, an unwitting colonizer. The case of Quintana Roo, Mexico illustrates how the tourist can be seen as a pawn in a larger political project. Exposure of this predatory nature of tourism reveals processes that have implications for other Native regions of the Americas and beyond that are suffering similar “invasions.”

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.240
Threshold uncertainty score0.477

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.002
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.011
GPT teacher head0.247
Teacher spread0.237 · 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 designObservational
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

Citations14
Published2013
Admission routes2
Has abstractyes

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