MétaCan
Menu
Back to cohort
Record W4237616536 · doi:10.24124/2017/1356

Interruption of an immersive experience in an ethnic restaurant

2017· dissertation· en· W4237616536 on OpenAlexaffabout
Reza Akbar

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldPsychology
TopicMultisensory perception and integration
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsDistractionPsychologySustainabilityContext (archaeology)MarketingQualitative researchEthnic groupApplied psychologyAdvertisingSocial psychologyBusinessCognitive psychologyGeographySociology

Abstract

fetched live from OpenAlex

The service industry has evolved towards experience immersion, representing a viable strategy for long-term sustainability of food service – one of the top five industries providing the highest employment across Canada. This topic is explored from two perspectives, (1) the context of ethnically-themed restaurant operations, and (2) cognition as related to the attention of diners to aspects of their restaurant experience and effects of distraction on these attentional processes. This study investigates whether distraction by personal electronic devices (PEDs) reduces diners’ attention to their experience. The hypothesis was tested across two interrelated studies. Study One is comprised of qualitative work to develop a bank of questionnaire items that assess the range of content and a scaling model for the dependent variables assessing diners’ attention. Study Two investigates the dimensionality of the items comprising the dependent measures. Results suggest that, rather than a distraction, PED usage might enhance diners’ sensory immersive experience.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.268
GPT teacher head0.536
Teacher spread0.267 · 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
Published2017
Admission routes2
Has abstractyes

Explore more

Same topicMultisensory perception and integrationFrench-language works237,207