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Record W6944371631 · doi:10.18712/nsd-nsd2422-2-v2

Travel and Holiday Survey 2016, 3rd quarter, Travels

2023· dataset· en· W6944371631 on OpenAlexaboutno aff

Bibliographic record

VenueNSD – Norsk senter for forskningsdata · 2023
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)ContinuationSurvey data collectionImmigrationTravel medicineWork (physics)Travel behaviorPublic transport

Abstract

fetched live from OpenAlex

The "Travel and Holiday Survey 2016, 3rd quarter" is a continuation of Statistics Norway's omnibus survey conducted in the years 1992-2004. Four annual data collections are conducted, one for each quarter. The main purpose is to map Norwegians' travel habits, as well as collecting data for other official statistics. In addition to data on Norwegians' travel habits, data on substance abuse, attitude towards immigration, influenza vaccine, etc. are also collected. Some themes are repeated every quarter while others are included only once. This is the travel file. A person file is also available for order. An overview of subjects covered in the third quarter follows below. - Household - Work and employment - Travels with atleast one overnight stop - Attitudes towards immigration - Alcohol and nicotine habits - Health and vaccination - Income

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.170
Threshold uncertainty score0.339

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.006
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0280.031

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 head0.063
GPT teacher head0.320
Teacher spread0.257 · 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 designObservational
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
Published2023
Admission routes1
Has abstractyes

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