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Record W1936787465 · doi:10.1177/001979391306600105

Unionization and Certified Sickness Absence: Norwegian Evidence

2013· article· en· W1936787465 on OpenAlexaboutno aff
Arne Mastekaasa

Bibliographic record

VenueIndustrial and Labor Relations Review · 2013
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsnot available
Fundersnot available
KeywordsNorwegianDifferential (mechanical device)Demographic economicsDifferential effectsLabour economicsEconomicsMedicineEngineering

Abstract

fetched live from OpenAlex

The author analyzes the relationship between unionization and absences due to sickness in Norway using register data covering the entire population employed in the private sector during 2003 to 2007. A few previous studies have shown much higher sickness absence among unionized than nonunionized employees in Canada, the United States, and the United Kingdom. The author shows this is also true for Norway, although primarily for employees in lower grade jobs. The differential persists even when employees in the same detailed job category within the same establishment are compared using fixed effects models. Thus, differences between more or less unionized establishments, or differences in working conditions between members and nonmembers, fail to explain the differential. Analyses using individual fixed effects indicate that up to half of the union-nonunion differential in absence is due to selection on stable individual characteristics.

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)0.004
metaresearch head score (Gemma)0.010
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: none
Teacher disagreement score0.060
Threshold uncertainty score0.119

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.140
GPT teacher head0.397
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
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

Citations24
Published2013
Admission routes1
Has abstractyes

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