Volunteer Tourism has Gone Commercial: The Reasons and the Implications
Bibliographic record
Abstract
In the artilce 'Towards an Understanding of the Drivers of Commercialization in the Volunteer Tourism Sector, published in Tourism Recreation Research Vol. 37(2), 2012 ,' Coghlan and Noakes provide valuable perspective on the increasing commercialization of volunteer tourism (VT). This trend has been observed primarily with disapproval, but Coghlan and Noakes draw upon the broader universe of non-profit research to show that there are some valid reasons why non-profit VT organizations are commercializing. The paper therefore offers a noteworthy contribution to the subject, yet it is nonetheless worthwhile to reconsider and expand upon some of the key topics discussed. It is easy to lose sight of the unglamorous fundraising side of non-profits, so Coghlan and Noakes' first three drivers (VT organizations address complex issues, operate within a competitive sector, and manage multiple stakeholders) offer a useful reminder that non-profits require money to operate and this money is not always easily available. The three drivers are certainly valid for some organizations, but it should be emphasized that this applicability is limited. For example, claiming VT organizations address complex issues somewhat exaggerates many organizations' efforts by alluding to broader issues (e.g., education) instead of the simpler, less financially demanding goals generally espoused by VT projects (e.g., teaching English in a school). Likewise, the authors themselves acknowledge that as VT organizations commercialize they acquire a particularly complicated stakeholder group (i.e., volunteer tourists), which may even increase stakeholder management challenges. Furthermore, Coghlan and Noakes' list of moneyrelated drivers appears rather incomplete; possible additions could include the fickleness of alternative funding sources (e.g., individual donations, corporate gifts, government grants, etc.) and the potential to boost fundraising via exposure through VT.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".