Decolonising tourism and development: from orphanage tourism to community empowerment in Cambodia
Bibliographic record
Abstract
Tourism has been viewed as a development pathway, with alternative tourisms such as volunteer tourism perceived as promising. However, critics have highlighted how white saviourism and Western ideologies of superiority may underpin both development agendas and activities like volunteer tourism. The COVID crisis has impacted both tourism and international development and calls for rethinking. This case study is situated at the intersections of tourism, development and humanitarianism. It charts the evolution of the Cambodian Children’s Trust which emerged in 2007 from the co-founding of an orphanage by an Australian volunteer tourist and a local Khmer leader. Through a process of conscientisation, the orphanage has given way to a community development approach under the leadership of a 100 percent Khmer team in country, leaving footprints of empowering spaces rather than dependency structures. This article addresses the research question of how might we transform the paternalistic desire to “do good” found in both voluntourism and development into a practice of mutual solidarity? Illuminating issues of power and inequality in Western-led models, this article offers a framework for more just partnerships based on Freirian praxis: dialogue building critical consciousness, co-development of transformative praxis, capacity sharing and trust in the capabilities of the people.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".