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Record W4411345162 · doi:10.3384/rela.2000-7426.5869

Swedish adult education policy and the world of work

2025· article· en· W4411345162 on OpenAlexaff
Erik Nylander, Kjell Rubenson

Bibliographic record

VenueEuropean Journal for Research on the Education and Learning of Adults · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsUniversity of British Columbia
FundersVetenskapsrådetLinköpings Universitet
KeywordsAdult educationWork (physics)Lifelong learningPolitical sciencePedagogySociologyEconomic growthEconomicsEngineering

Abstract

fetched live from OpenAlex

Public policies in adult education have been shaped by two generations of human capital influence, each reflecting different assumptions about the relationship between education and the economy. While the first generation, emerging in the post-war decades, has received little scholarly critique, the second has faced extensive criticism for its neoliberal orientation. Tracing these lines of thought internationally, we emphasise the need for a broader historical-institutional perspective to understand what was at stake and why the world of work remains a central component of contemporary Adult Learning and Education (ALE) policy. Using historical institutionalism and the concept of pressure points, we analyse Swedish adult education policy from the late 1950s onward. We show how its adult education system, shaped by both economic and social demands, emerged and has remained largely intact despite grand societal changes and political shifts. At the same time, evolving policies have paved the way for private providers within welfare services, hybridising the Swedish welfare-state-model.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.026
Threshold uncertainty score0.132

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0050.011
Scholarly communication0.0110.003
Open science0.0010.003
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.066
GPT teacher head0.471
Teacher spread0.405 · 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 designQualitative
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

Citations1
Published2025
Admission routes1
Has abstractyes

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