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Record W6963062196 · doi:10.18712/nsd-nsd2136-v2

Adult Literacy and Life Skills Survey (ALL) 2003

2015· dataset· en· W6963062196 on OpenAlexaboutno aff

Bibliographic record

VenueNSD – Norsk senter for forskningsdata · 2015
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsNorwegianCompetence (human resources)LiteracyEveryday lifeReading (process)Survey data collectionAdult literacyFunctional illiteracyLife skills

Abstract

fetched live from OpenAlex

During 2003, Statistics Norway (SSB) in cooperation with the Reading Centre at the University of Stavanger, conducted a reading comprehension survey (Leseforståelsesundersøkelsen, LFU). LFU is part of an international research program, Adult Literacy and Life Skills Survey (ALL). LFU is an ambitious project with the aim of collecting comparable data on adults in Norway. In Norway, the survey conducted in Norwegian (bokmål) regardless of the informant's mother tongue. ALL is internationally led by Statistics Canada in cooperation with the Educational Testing Service (ETS) in the United States. The pages of competence here captured, is what in English is called "literacy" (OECD and Statistics Canada 1995): The ability to use printed or handwritten texts: - To function in society and meet the requirements for various everyday situations - In order to achieve their needs and personal goals - To be developed in line with their personal circumstances This survey was carried out in Norway in the language form; bokmål, regardless of the informant's mother tongue.

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)0.006
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 score0.181
Threshold uncertainty score0.359

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.007
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.012

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 head0.033
GPT teacher head0.328
Teacher spread0.295 · 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".

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Citations0
Published2015
Admission routes1
Has abstractyes

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