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Record W4412904713 · doi:10.3389/fpsyg.2025.1656269

Editorial: Exploring heavy work investment: multidimensional constructs and work outcome variance

2025· editorial· en· W4412904713 on OpenAlexaff
Christian Vandenberghe, Aharon Tziner, Julio César Acosta Prado

Bibliographic record

VenueFrontiers in Psychology · 2025
Typeeditorial
Languageen
FieldPsychology
TopicWorkaholism, burnout, and well-being
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsPsychologyWork (physics)Variance (accounting)Outcome (game theory)Investment (military)Social psychologyApplied psychologyMicroeconomicsEconomicsPolitical scienceAccounting

Abstract

fetched live from OpenAlex

studies (71,625 participants) across 23 countries. With an overall pooled prevalence of 15.2%, the analysis reveals that roughly one in seven employees may be affected by workaholism. Metaregression showed that studies using nationally representative samples reported significantly lower prevalence (around 9.8%), while non-representative samples yielded higher estimates. Triad personality traits (narcissism, Machiavellianism, and psychopathy) interacted with career interests to predict subjective and objective career success among 300 South African professionals. They found that the association between narcissism and higher career success is amplified for individuals with enterprising interests, while Machiavellianism was linked to better success when social interests were high. In contrast, psychopathy showed limited predictive value for career outcomes regardless of interest type. The study situates its findings within personenvironment fit theory, highlighting that Dark Triad traits can be leveraged adaptively depending on one's vocational interests. The authors acknowledge limitations to their study, including crosssectional design and self-report measures, and call for longitudinal and multi-method approaches to clarify causal pathways.

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), Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.022
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0040.004
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 head0.021
GPT teacher head0.314
Teacher spread0.293 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEditorial

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

Citations0
Published2025
Admission routes1
Has abstractyes

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