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Record W2725346411 · doi:10.1111/boer.12114

WHAT DETERMINES VACATION LEAVE? THE ROLE OF GENDER

2017· article· en· W2725346411 on OpenAlexaboutno aff
Ali Fakih

Bibliographic record

VenueBulletin of Economic Research · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsWork (physics)Promotion (chess)Set (abstract data type)Working timeWorking hoursDemographic economicsBusinessMarketingLabour economicsEconomicsComputer sciencePolitical scienceEngineering

Abstract

fetched live from OpenAlex

ABSTRACT Vacation leave is introduced in workplaces to improve the working environment. Surprisingly, it has been observed that a large number of workers do not use all of their entitled vacation days. This paper provides a novel set of facts about the gender differences in taking vacation time using the Canadian Workplace Employee Survey, which is a linked longitudinal employer‐employee dataset. The results show considerable differences between men and women in the estimated effects of some demographic characteristics after controlling for job and workplace characteristics. However, they reveal significant implications of work arrangements (e.g., part‐time work, flexible work schedules, and home‐based work), job promotion, supervisory tasks, and union membership for vacation use, for both men and women. This paper provides further insights on the use of fringe benefits that may be useful to policymakers and businesses.

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)0.008
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 score0.061
Threshold uncertainty score0.121

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.154
GPT teacher head0.420
Teacher spread0.266 · 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

Citations12
Published2017
Admission routes1
Has abstractyes

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