For and by the People? Internal Versus External Slum Tourism Entrepreneurs’ Impacts
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".