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Record W1655573981

Employee-Friendly and Employer-Friendly Non-Standard Work Schedules and Locations

2008· article· en· W1655573981 on OpenAlexaboutno aff
Gordon B. Cooke, Işık U. Zeytinoglu, Naresh C. Agarwal, Joseph B. Rose

Bibliographic record

VenueInternational journal of employment studies · 2008
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsnot available
Fundersnot available
KeywordsJob satisfactionWork (physics)MarketingFamily-friendlyBusinessEnvironmentally friendlyEmployee researchProject commissioningPublishingEmployee resource groupsPublic relationsOperations managementEmployee engagementManagementEngineeringEconomicsPolitical science
DOInot available

Abstract

fetched live from OpenAlex

While many studies suggest that non-standard work schedules and locations have negative implications for workers, some indicate positive impacts. The purpose of this paper is to examine the relationship of these non-standard work arrangements (NSWAs) with job satisfaction, after categorizing the former as being employee-friendly or employer-friendly. This supply-side analysis of the labour market has a quantitative research design, and utilizes Statistics Canada's 2003 Workplace and Employee Survey (WES) data. As hypothesized, the incidence of employee-friendly non-standard work schedules and locations is significantly and positively related to job satisfaction while incidence of employer-friendly examples is significantly and negatively related to job satisfaction. In today's business climate, employers have the strategic choice to utilize NSWAs to address their operational needs, or the needs of their workers. Although either might make strategic sense, implementing employee-friendly NSWAs potentially benefits both parties concurrently.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.068
GPT teacher head0.420
Teacher spread0.352 · 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 teacher head, not a consensus.

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

Citations4
Published2008
Admission routes1
Has abstractyes

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