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Record W2011883608 · doi:10.1080/09669582.2014.902065

Motivation-based transformative learning and potential volunteer tourists: facilitating more sustainable outcomes

2014· article· en· W2011883608 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Sustainable Tourism · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicTourism, Volunteerism, and Development
Canadian institutionsnot available
Fundersnot available
KeywordsTransformative learningTourismHospitalityPsychologyVolunteerPublic relationsAltruism (biology)PopularityMarketingSociologySocial psychologyPedagogyPolitical scienceBusiness

Abstract

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AbstractTransformative learning (TL) is an important component of sustainable volunteer tourism experiences, potentially reducing unsustainable outcomes, and educating and enlightening volunteers. This paper reviews theories and issues about TL in volunteer tourism, and analyzes data from 1008 useable responses to an online survey of potential volunteer tourists. A factor–cluster analysis of potential volunteer tourists' motivations identified key volunteer tourist segments and assessed differences in expectations of TL across each segment. Altruism remains the primary motivation, with personal development an expectation, but the study also found desires to experience different cultures, build relationships with family, and to escape one's daily life. Three motivation segments emerged: Volunteers, Voluntourists, and Tourists. Differences in the three clusters' expectations for TL were assessed through multiple analysis of variance using items representing Taylor's three elements of TL: self-reflection, engaging in dialogue, and intercultural experience.Differences in TL expectations varied significantly across the three segments. Potential Voluntourists were most likely to expect to participate in TL opportunities. The paper concludes with suggestions for maximizing TL for each segment. Volunteers and Tourists may require activities that include different, less obvious forms of TL. Volunteer tourism organizations need to invest significantly in staff training in TL.依据动机的转化学习和潜在旅游志愿者:促进更加可持续的结果转化学习是可持续旅游志愿者经历的重要组成部分,可能会减少非可持续行为,教育和启发志愿者。这篇文章参考了关于旅游志愿者在转化学习中的理论和存在问题,分析了从潜在旅游志愿者中收集的份可用网络调查问卷中得到的数据。用因子归类的分析方法分析了潜在旅游志愿者的动机,划分了主要的旅游者群体,并且检验了对不同群体在进行转化学习的期望差异性。利他主义仍然是主要的动机,个人发展需要也是一种动机,但是这篇文章也发现了对于体验不同文化、和家人建立良好关系和逃离日常生活的需求,出现了三种动机划分:志愿者、志愿旅游者和旅游者。通过变异数分析法,利用泰勒转化学习(2008)的三个要素:自我反思,参与对话和跨文化体验来评估三个群体在转化学习中的期望的不同。转化学习在这三个群体中的期望有很大不同。潜在旅游志愿者最有可能参加转化学习。这篇文章得出结论并对怎样最大化每个群体的转化学习结果提出建议。志愿者和游客可以在不同的,形式不明显的活动中进行转化学习。旅游志愿者组织需要在员工转化学习训练中投入更多的资金。Keywords: transformative learningvolunteer tourismsegmentationexpectations关键词: 转化学习旅游志愿者划分期望 Notes1. For a full discussion of Mezirow's 10 phases of transformative learning, see Coghlan and Gooch Citation(2011).2. Volunteer tourism was referred to and labeled as voluntourism in the survey instrument.Additional informationNotes on contributorsWhitney KnollenbergWhitney Knollenberg is a PhD student in the hospitality and tourism management program at Virginia Tech, USA. She earned her MS in sustainable tourism from East Carolina University. Her research interests include tourism's impact on communities, tourism planning, and the role of policy, power, and partnerships in tourism development.Nancy G. McGeheeNancy McGehee received her MS and PhD in sociology from Virginia Tech in 1995 and 1999, respectively. She previously worked in the regional rural tourism development field. She is an associate professor and the Willard and Alice S. Marriott Junior Faculty Fellow of hospitality management in the Hospitality and Tourism Management Department at Virginia Tech. Her academic career has focused on the volunteer tourism segment of the industry. She has worked with numerous volunteer tourism organizations throughout the USA, in Mexico, and most recently in Haiti.B. Bynum BoleyB. Bynum Boley, PhD, is an assistant professor of natural resources, recreation, and tourism at the Warnell School of Forestry and Natural Resources at the University of Georgia, USA. His research focuses on sustainable tourism with a special attention to how the natural and cultural resources of a destination influence competitiveness, and how increased awareness of the economic value of natural and cultural resources influences policy and management decisions about sustainable development.David ClemmonsDavid Clemmons is the founder of VolunTourism.org., working with academics and practitioners in shaping the voluntourism industry. He is the publisher and editor of the quarterly The VolunTourist Newsletter and The VolunTourist Weekly Review, and he informs stakeholders, the media, academics, and the public on emerging practices within the voluntourism community. He is a recognized global thought leader on voluntourism, and his opinions and insights are reflected in numerous other publications. Since 2000, he has consulted with the tourism industry, NGOs, and the public sector in such places as Argentina, Bolivia, Canada, Jordan, Mexico, and the United States.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.154
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.258
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it