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For and by the People? Internal Versus External Slum Tourism Entrepreneurs’ Impacts

2024· article· en· W4402202181 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTourism Analysis · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsTourismSlumBusinessMarketingEconomic geographyGeographySociology

Abstract

fetched live from OpenAlex

The controversial impacts of slum tourism have sparked debate and raised questions about its benefits for impoverished communities. The potential positive effects of slum tourism often hinge on “last mile” strategies and the crucial role of local entrepreneurs who manage the visits and interactions in determining the benefits to these areas. Drawing on a blend of Social Entrepreneurship Theory and Economic Development Theory, we explore and compare the contributions of both internal and external slum tourism entrepreneurs. Our findings reveal striking differences in their strategies and orientations. For example, internal entrepreneurs are deeply rooted in the focal slum and prioritize long-term poverty alleviation through the creation of permanent jobs and innovative approaches, whereas external entrepreneurs tend to focus on profit maximization and diversification of their offer outside the slums. However, both internal and external entrepreneurs actively challenge stereotypes, catalyze skills’ development, and channel resources back into the slum communities. This research sheds light on the multifaceted impacts of slum tourism entrepreneurship, providing critical insights for future endeavors in community development and slum tourism studies.

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.

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 categoriesScholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.102
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.341
Teacher spread0.322 · 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