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Record W3125918535 · doi:10.22215/etd/2013-09999

Developing the Volunteer Tourist Identity through Meaningful Interaction: A Critical Comparison of the Lived Experiences of Childcare and Animal Care Volunteers in South Africa

2013· dissertation· en· W3125918535 on OpenAlexaff
Jennifer Perry

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicTourism, Volunteerism, and Development
Canadian institutionsCarleton University
Fundersnot available
KeywordsTourismEthnographyVolunteerParticipant observationSymbolic interactionismIdentity (music)Social identity theoryPsychologyCritical ethnographySociologySocial psychologyPublic relationsSocial groupSocial scienceGeographyPolitical scienceAnthropologyEcology

Abstract

fetched live from OpenAlex

This thesis employs ethnographic research techniques to examine the lived experiences of individuals who participate in volunteer tourism projects.Data was gathered over a four month period from two diverse programs in South Africa, one working with children and one working with animals.Using a symbolic interactionist theoretical approach, the analysis of participant observation and interview data reveal an interaction process at the structural, group and individual level through which program participants come to identify self as a volunteer tourist and form volunteer tourist identities appropriate to the social situation experienced within their own particular volunteer project.The findings also suggest program culture and group norms, as well as altruistic and personal motivations, may be influential in affecting volunteer tourist behaviour.Results of this study highlight the need for further cross-comparative ethnographic research on divergent programs to enhance the understanding of volunteer tourist experiences and volunteer tourism as a whole.vii

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 imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0160.018
Scholarly communication0.0070.004
Open science0.0010.011
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.040
GPT teacher head0.353
Teacher spread0.313 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

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