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Record W4381250873 · doi:10.1016/j.heliyon.2023.e17447

The social construction of workaholism as a representational naturalization

2023· article· en· W4381250873 on OpenAlexafffund
Lilian Negura, Nathalie Plante, Dahlia Namian

Bibliographic record

VenueHeliyon · 2023
Typearticle
Languageen
FieldPsychology
TopicWorkaholism, burnout, and well-being
Canadian institutionsUniversité du Québec à MontréalUniversity of Ottawa
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsNaturalizationSituatedSociologyAddictionRepresentation (politics)PsychologySocial representationSocial psychologyEpistemologyPoliticsComputer scienceAlien

Abstract

fetched live from OpenAlex

Workaholism, a term borrowed from the language around alcoholism, first appeared in academic writing in the late 1960s. This article addresses the following questions: How has the concept of workaholism evolved in scientific literature and in society? How do people who identify as workaholics represent and communicate work addiction, and how do they identify it as their lived reality? Drawing on the concept of naturalization as a process of social representation, we argue that workaholism has been constituted as a naturalized object, and we consider the ways in which it is reproduced in everyday life through communication and experience. We situated the definition of workaholism within the scholarly literature. We then conducted semi-structured interviews with eleven individuals who self-identify or have been diagnosed as work addicts. Our research shows that representational naturalization began when workaholism first became a recognizable reality as a result of changes in the world of work. Naturalization was then achieved by eliminating contradictions through the process of decoupling the positive features of workaholism from the overall concept. Our results demonstrate how this naturalized representation of workaholism is reproduced through the communication and lived experience of "workaholics."

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.755
Threshold uncertainty score0.632

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.020
GPT teacher head0.353
Teacher spread0.333 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations13
Published2023
Admission routes2
Has abstractyes

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