Assessing the impacts of international volunteer tourism in host communities: a new approach to organizing and prioritizing indicators
Bibliographic record
Abstract
This paper explores the use of indicators to evaluate the impacts of volunteer tourism in host communities, based on an online questionnaire sent to 183 volunteer tourism organizations. Little research exists demonstrating how volunteer tourism programs impact host communities or how impacts can be assessed, but the literature suggests the use of indicators to do so. Social indicator research and systems thinking assert that impact evaluation must be comprehensive and that indicators must consider interconnectivities present in the tourist system; we propose a framework of indicator development that addresses this. Data analysis focuses on volunteer tourist activities and how organizations prioritize indicators to assess diverse impacts of volunteer tourism in host communities. Comparisons are drawn between organizations in Latin America and international organizations (based in the USA, Canada, the UK, Australia and New Zealand) that send volunteers abroad. Differing volunteer activities suggest unique approaches between in-country and international organizations. The usefulness and degree of assessment of diverse indicators of the local impacts of volunteer tourism are quantified, while discrepancies between indicator usefulness and assessment raise questions. Comparisons between international and in-country organizations, large and small organizations, and organizations focusing on long-term vs. short-term trips suggest differing organizational priorities and impacts of volunteer tourism.
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 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 | 0.020 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".