MétaCan
Menu
Back to cohort
Record W6950504154 · doi:10.5683/sp3/3nexzz

Canadian Travel Survey, 2004: Person Trip File

2004· dataset· en· W6950504154 on OpenAlexaffabout

Bibliographic record

VenueBorealis · 2004
Typedataset
Languageen
Field
Topic
Canadian institutionsStatistics Canada
Fundersnot available
KeywordsMicrodata (statistics)TourismCommissionAccommodationSurvey data collectionTravel surveyData sourceMeasure (data warehouse)

Abstract

fetched live from OpenAlex

The Canadian Travel Survey (CTS) is a major source of data used to measure the size and status of Canada's tourism industry. It was developed to measure the volume, characteristics and economic impact of domestic travel. It gathers data on more than 30 variables, including socio-economic profiles, trip characteristics, and expenditures. The CTS is conducted by Statistics Canada, as a supplement of the Labour Force Survey (LFS: Survey Number 3701), with the cooperation and support of the Canadian Tourism Commission (CTC) and ten provincial governments. The main users of the survey data are the CTC, the provinces, and tourism boards. Other users include the media, businesses, consultants and researchers. These files contain records which relate to the activities of Canadians travelling in Canada; origin and destination; volumes; nights away from home; length of stay; type of transportation; purpose of trip; accommodation used; expenditures by categories; and demographic characteristics. Included are the complete Canada microdata file on person-trips, household trips, person-nights, person and reallocated expenditures.

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)0.008
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: Dataset · Consensus signal: Dataset
Teacher disagreement score0.050
Threshold uncertainty score0.166

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0080.030
Science and technology studies0.0020.000
Scholarly communication0.0030.001
Open science0.0030.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0500.036

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 head0.035
GPT teacher head0.259
Teacher spread0.224 · 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
GenreDataset

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
Published2004
Admission routes2
Has abstractyes

Explore more

Same venueBorealisFrench-language works237,207