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Record W6930150079 · doi:10.5255/ukda-sn-5262-1

Great Britain Day Visits Survey, 2002-2003

2005· dataset· en· W6930150079 on OpenAlexaboutno aff

Bibliographic record

VenueVocBench (University of Rome Tor Vergata) · 2005
Typedataset
Languageen
FieldHealth Professions
TopicMedical Coding and Health Information
Canadian institutionsnot available
Fundersnot available
KeywordsTRIPS architectureDestinationsNames of the days of the weekScale (ratio)Time of dayQuarter (Canadian coin)

Abstract

fetched live from OpenAlex

The main aim of the <i>United Kingdom Day Visits Survey</i>, the <i>Great Britain Day Visits Survey</i> (GBDVS), and latterly the <i>England Leisure Visits Survey</i> (ELVS), is to measure the extent of participation in day visits, and to estimate the scale and value of visits taken. In particular the principal investigators are interested in the extent of participation in different kinds of day trips, how frequently particular types of trip are undertaken, and associated expenditure.<br> <br> The survey also seeks to provide information on a number of other trip details, such as activities undertaken, areas visited, time spent at the main destination, modes of transport, distance travelled, number of people involved and the trip party composition. Respondents to the survey are generally asked to recall trips taken within the past two weeks.<br> <br> The 2002-2003 survey covered trips within Great Britain only, not Northern Ireland as had previously been the case, hence the change of name from <i>United Kingdom Day Visits Survey</i> to <i>Great Britain Day Visits Survey</i>.<br> <br> The survey also sought to provide estimates of leisure day visits to three main types of destination, including towns and cities, the countryside, and the seaside or coast. Within these types, trips involving visits to any of three further subsidiary destinations (woods and forests, and both navigable and non-navigable inland waters) could also be recorded.

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 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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.027
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0340.007

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 head0.123
GPT teacher head0.363
Teacher spread0.240 · 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; both teacher heads agree on what is shown here.

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
Published2005
Admission routes1
Has abstractyes

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