MétaCan
Menu
Back to cohort

Reimagining the Geography of the Favelas: Pacification, Tourism, and Transformation in Complexo Do Alemão, Rio de Janeiro

2015· article· en· W2060648068 on OpenAlexfundno aff
Emily LeBaron

Bibliographic record

VenueTourism Review International · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsnot available
FundersSimon Fraser University
KeywordsTourismPopulationPovertySociologyPolitical scienceEconomic growthEconomyLawEconomics

Abstract

fetched live from OpenAlex

This article examines the recent intersection of two forces, at times complementary and at times competing: pacification and favela tourism in Rio de Janeiro, Brazil. Rio's favelas have long been considered archetypal neighborhoods of poverty and crime. The city's new “pacification” project involves military and police occupation of targeted communities, to control drug cartel-related violence. Complexo do Alemão is a large cluster of 15 favelas in Rio's North Zone with a particularly violent history, officially “pacified” since 2010. Together, tourism and pacification are transforming Alemão at a rapid pace, both materially and discursively. This article involves a comprehensive look at the budding favela tourism industry in Complexo do Alemão, and incorporates results from 2013 field research there, including interviews with residents and guides. Favela tourism in Alemão has seen mixed success, and many companies are struggling; still, it brings unique benefits to the local population, such as protection, accountability, and a means of reclaiming occupied space. In addition, favela tourism is an integral tool to tackling the stigmatization of favela residents as talentless criminals, part of a larger trend that is reshaping the meaning of “favela” in the geographical imagination.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.860
Threshold uncertainty score0.346

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.048
GPT teacher head0.356
Teacher spread0.308 · 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 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

Citations4
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueTourism Review InternationalSame topicDiverse Aspects of Tourism ResearchFrench-language works237,207