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Record W2164659489 · doi:10.1002/hrm.20226

The inclusion challenge with reduced‐load professionals: The role of the manager

2008· article· en· W2164659489 on OpenAlexaffabout
Pamela Lirio, Mary Dean Lee, Margaret L. Williams, Leslie K. Haugen, Ellen Ernst Kossek

Bibliographic record

VenueHuman Resource Management · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsMcGill University
Fundersnot available
KeywordsWork (physics)ProductivityInclusion (mineral)Identification (biology)PsychologyBusinessPublic relationsMarketingPolitical scienceSocial psychologyEconomicsEconomic growthEngineering

Abstract

fetched live from OpenAlex

Abstract Increased interest in reduced‐load (part‐time) work among professionals who want to have a life beyond work has led to new challenges for managers who must sustain productivity while also supporting employees. However, to date, little attention has been focused on exactly how managers facilitate effective implementation of these alternative work arrangements. This study presents findings from an interview study of 83 cases of reduced‐load professionals in 43 organizations in the United States and Canada. Analysis of the interviews with both professionals and their managers surfaced recurrent themes that led to identification of five clusters of behaviors and five clusters of dispositions that capture the nature of managerial support in implementing reduced‐load work. The ten categories of behaviors and dispositions expand on existing notions of supervisory support and provide new insight into the role of managers in fostering inclusiveness. Additional quantitative analyses found significant relationships between the success of the reduced‐load arrangements and specific managerial behaviors and dispositions. © 2008 Wiley Periodicals, Inc.

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)0.006
metaresearch head score (Gemma)0.016
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0120.008
Scholarly communication0.0050.002
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.280
Teacher spread0.258 · 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

Citations109
Published2008
Admission routes2
Has abstractyes

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