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Record W4210268434 · doi:10.1177/03128962211073021

The importance of relational work design characteristics: A person-centred approach

2022· article· en· W4210268434 on OpenAlexaff
Caroline Knight, Matthew J. W. McLarnon, Ramon Wenzel, Sharon K. Parker

Bibliographic record

VenueAustralian Journal of Management · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsMount Royal University
FundersAustralian Research Council
KeywordsAutonomySupervisorWorkloadSocial psychologyProsocial behaviorBeneficiaryWork (physics)PsychologyTask (project management)BusinessManagementEconomicsEngineeringFinancePolitical science

Abstract

fetched live from OpenAlex

Adopting a person-centred approach, we integrate the job demands-control-support model with relational work design theory to investigate employee work design profiles involving autonomy, workload, social support and prosocial characteristics (representing the combined influence of task significance and beneficiary contact). For a sample of Australian not-for-profit employees ( N = 2421), we identified four work design profiles: ‘active connected’, ‘passive disconnected’, ‘high strain disconnected’ and ‘controlled disconnected’. The most favourable profile, active connected, demonstrated the highest vigour and social worth, and was predicted by people being in higher managerial positions and having permanent employment contracts. The high strain disconnected and controlled disconnected profiles were associated with greater psychological exhaustion. Longer working hours predicted membership of the high strain disconnected profile. JEL Classification: L31, L30, L20, L29

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)0.005
metaresearch head score (Gemma)0.012
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 score0.007
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.003
Science and technology studies0.0010.004
Scholarly communication0.0070.003
Open science0.0020.004
Research integrity0.0010.001
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 head0.058
GPT teacher head0.229
Teacher spread0.171 · 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

Citations10
Published2022
Admission routes1
Has abstractyes

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