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Record W3122809401

Ageing and Skills: The Case of Literacy Skills

2019· article· en· W3122809401 on OpenAlexaffabout
Garry F. Barrett, W. Craig Riddell

Bibliographic record

VenueRePEc: Research Papers in Economics · 2019
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLiteracyAgeingCognitive skillCohortPsychologyPopulation ageingSample (material)PopulationDevelopmental psychologyDemographyCognitionGerontologyMedicineSociologyPedagogy
DOInot available

Abstract

fetched live from OpenAlex

The relationship between ageing and skills is of growing policy significance due to population ageing, the changing nature of work and the importance of literacy for social and economic well-being. This article examines the relationship between age and literacy skills in a sample of OECD countries using three internationally comparable surveys. By pooling the survey data across time we can separate birth cohort and ageing effects. In doing so we find literacy skills decline with age and that, in most of our sample countries, successive birth cohorts tend to have poorer literacy outcomes. Therefore, once we control for cohort effects the rate at which literacy proficiency falls with age is much more pronounced than that which is apparent based on the cross-sectional relationship between age and literacy skills at a point in time. Further, in studying the literacy-age relationship across the skill distribution in Canada we find a more pronounced decline in literacy skills with age at lower percentiles, which suggests that higher initial literacy moderates the influence of cognitive ageing.

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)0.013
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0010.007
Scholarly communication0.0030.005
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.013
GPT teacher head0.342
Teacher spread0.329 · 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
GenreEmpirical

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
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueRePEc: Research Papers in Economics→Same topicReading and Literacy Development→French-language works237,207→