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Record W1966950872 · doi:10.1080/13032917.2008.9687070

Predictors of Commitment to Careers in the Tourism Industry

2008· article· en· W1966950872 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAnatolia · 2008
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicHospitality and Tourism Education
Canadian institutionsYork University
Fundersnot available
KeywordsTourismHospitalityWorkforceHospitality industryWork (physics)BurnoutHospitality management studiesMarketingWork engagementPublic relationsBusinessHuman resourcesPsychologyManagementPolitical scienceEconomic growthEconomicsEngineering

Abstract

fetched live from OpenAlex

ABSTRACT The hospitality and tourism industry is a significant contributor to the economies of many countries. As a result, countries need an educated, skilled and committed workforce to be successful. To fill this need, colleges and university have developed programs of study to improve the quality of human resources working in this industry. This study considers predictors of comment to a career in hospitality and tourism among 640 male and 375 female university tourism students in Turkey. Three types of predictors were examined using hierarchical regression analyses: work values, levels of student engagement during their program of study, and levels of student burnout during their university studies. Work values were unrelated to commitment to a career in hospitality and tourism; students' reporting higher levels of engagement, and those reporting lower levels of burnout, were more committed to careers in tourism. Implications of these findings for university tourism programs and employers of graduates of university tourism programs are offered.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.550

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.020
GPT teacher head0.240
Teacher spread0.220 · 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