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Record W7038776835

Knowledge sharing and quality assurance in hospitality and tourism

2013· book· en· W7038776835 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

VenueQueensland's institutional digital repository (The University of Queensland) · 2013
Typebook
Languageen
FieldEngineering
TopicEngineering and Test Systems
Canadian institutionsnot available
Fundersnot available
KeywordsTourismKnowledge sharingKnowledge value chainHospitalityHospitality industryCompetitive advantageKnowledge economyOrganizational learning
DOInot available

Abstract

fetched live from OpenAlex

\n\t\t\t\t\tLearn both theory and practice of knowledge management. Sir Francis Bacon once wrote, "Knowledge is power." Knowledge Sharing and Quality Assurance in Hospitality and Tourism provides strategies to grab that power and the competitive edge in the tourism industry through knowledge management (KM) and quality assurance. Leading tourism and hospitality experts offer the latest theory and practical frameworks to expand the knowledge needed for creating and maintaining success at destinations around the world. Each cogent chapter provides fresh directions for future research and the creation of effective ways to share and use knowledge. As the tourism and hospitality industry expands, the competition increases as the search continues for ways to ensure quality, know the consumer, and discover the best standards of destination operation. Knowledge Sharing and Quality Assurance in Hospitality and Tourism is a unique foundational text that clearly explains the theory and practical management of knowledge in this lucrative, very competitive industry. Knowledge theory is used to explore organizational functioning, change issues, and operations at destinations in industry clusters and networks. Chapters are extensively referenced. Topics in Knowledge Sharing and Quality Assurance in Hospitality and Tourism include: • the role of higher education in transferring knowledge into practice. • four kinds of benchmarking. • e-mail response quality. • quality management at the destination level and its path to knowledge sharing. • tourism managers knowledge needs-the knowledge type, where the knowledge is available, and sharing that knowledge between academics and the industry. • strategic planning in knowledge management. • three element framework of knowledge management assessment. • a case study of an international tourism project and the use of knowledge management. • a case study of best practice in tourism research dissemination in Quebec and Queensland. Knowledge Sharing and Quality Assurance in Hospitality and Tourism is crucial, idea-sparking reading perfect for tourism researchers, tourism managers, administrators, educators, and students.\n\t\t\t\t

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.581
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.011
GPT teacher head0.193
Teacher spread0.181 · 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