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Record W4234073920 · doi:10.18280/ijsdp.160210

Study on Quality Evaluation of Taiwan's B&B Based on Content Analysis Method and Its Implications

2021· article· en· W4234073920 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.

venuePublished in a venue whose home country is Canada.
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

VenueInternational Journal of Sustainable Development and Planning · 2021
Typearticle
Languageen
FieldComputer Science
TopicTechnology and Data Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsMainland ChinaChinaIndex (typography)Operations managementStatisticsMathematicsComputer scienceEconomicsGeography

Abstract

fetched live from OpenAlex

Taiwan has been nearly 40 years of history in developing B&B, and has accumulated rich experience in B&B development and management. While B&B history is much shorter in mainland China, there is a gap of industry matureness between two sides. Therefore, it is of great practical significance for the development of B&B in mainland China to systematically discuss the quality of B&B in Taiwan. This study extracted 24 B&B cases in Taiwan from Agoda website, based on stratified sampling method, and classified and assigned values according to the quality evaluation index and coding standard with content analysis method. Finally, the paper obtained the key factors of the quality of the B&B based on the evaluation of indicators at all levels, and got the correlation analysis results: cost performance and fitment time are positively correlated to the quality of the B&B while guestroom number is negatively correlated to the quality of the B&B.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.236
Threshold uncertainty score0.296

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.155
GPT teacher head0.418
Teacher spread0.262 · 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