MétaCan
Menu
Back to cohort
Record W218650194 · doi:10.1079/9781780648873.0128

How to conduct personal interviews and focus groups.

2017· book-chapter· en· W218650194 on OpenAlexaff
S. L. J. Smith

Bibliographic record

VenueCABI eBooks · 2017
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicHospitality and Tourism Education
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsFocus groupInterviewContext (archaeology)Focus (optics)TourismCoding (social sciences)Engineering ethicsPsychologySociologyPolitical scienceSocial scienceEngineeringGeography

Abstract

fetched live from OpenAlex

Abstract This chapter provides an introduction to some basic principles and techniques associated with doing personal interviews in the context of tourism research, as well as the basics of coding and analysing the transcripts of interviews. The chapter also looks at a special form of interviewing: focus groups. Appropriate applications and limitations of personal interviews and focus groups are highlighted.

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)0.033
metaresearch head score (Gemma)0.072
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.114
Threshold uncertainty score0.380

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0330.072
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0040.003
Scholarly communication0.0040.006
Open science0.0030.005
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.1140.147

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 head0.058
GPT teacher head0.254
Teacher spread0.196 · 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 designNot applicable
Domainnot available
GenreMethods

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 routes1
Has abstractyes

Explore more

Same venueCABI eBooksSame topicHospitality and Tourism EducationFrench-language works237,207