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Record W6906854833 · doi:10.18712/nsd-nsd3077-v2

The Labor Force Survey 2013, additional survey on work accidents and occupational injuries/work-related health problems

2023· dataset· en· W6906854833 on OpenAlexaboutno aff

Bibliographic record

VenueNSD – Norsk senter for forskningsdata · 2023
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Occupational safety and healthWork (physics)Official statisticsPopulationWork forceHealth problemsWorking populationSurvey data collection

Abstract

fetched live from OpenAlex

Statistics Norway has carried out official quarterly labor force surveys (AKU) from and including the 1st quarter of 1972. The purpose is to give the labor authorities and other interested parties knowledge of the occupational structure of the population and the development over time. The surveys will provide a basis and statistical material for occupational forecasts and labor market research. From 1992, the committees in AKU are representative at county level. More specifically, this is an additional survey for the Labour Force Survey 2013. The questions concern, among other things, occupational accidents, work-related health problems and other conditions in the workplace. The weight "tuvekt" should be divided by 4,000 in this additional survey to the Labor Force Survey.

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)0.009
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 score0.115
Threshold uncertainty score0.230

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.009
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0400.042

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 head0.057
GPT teacher head0.336
Teacher spread0.279 · 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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Same venueNSD – Norsk senter for forskningsdataFrench-language works237,207