Increasing social responsibility in tourism based on volunteer tourism
Bibliographic record
Abstract
The aim of the paper is to determine the impact of volunteer tourism on the level of social responsibility in the tourism industry of Ukraine. The online survey included 440 respondents (77.3% women; 22.7% men). Physicians were asked about the importance of volunteering and their participation in the volunteer movement. According to the results of the survey, 59.1% of respondents do not have volunteering experience, 38.6% of respondents have episodic experience and only 2.3% of respondents constantly participate in volunteer activity. Although a quarter of respondents who do not have volunteering experience do not consider it appropriate to have such experience. This allowed us to identify the motives for engagement in volunteering and the factors hindering such activities. Participants were also asked about the impact of volunteer tourism on the prosperity of regional communities, building a democratic society, education of socially responsible citizens. The research has shown that for the development of volunteer tourism the most important is the promotion of volunteerism in society (61.4% of respondents) and cooperation with international organizations (50.0%). This allowed the authors to suggest directions and forms of international cooperation for the development of volunteer activities in the 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".