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The Effect of the Ideal Worker Norm on Employees

2021· article· en· W3183178435 on OpenAlexaboutno aff
Clarissa Rene Steele, Galina Boiarintseva, Vanessa Burke, Alyson Gounden Rock, Aurora Turek, Jennica R. Webster

Bibliographic record

VenueAcademy of Management Proceedings · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsnot available
Fundersnot available
KeywordsNorm (philosophy)Ideal (ethics)Social psychologyOvertimeAffect (linguistics)PsychologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

In today’s organizations, supervisors expect employees to be highly productive throughout their workday, no matter how long they work or when they work. These expectations have been termed the “ideal worker norm” (Davies & Frink, 2014). The ideal worker prioritizes their work over other life domains and shows this dedication to work by working overtime and non-traditional hours, being always available, visibly working (e.g., sending emails during non-work hours), and being unhampered by non-work obligations (Kelly et al., 2010; Reid, 2015; Sallee, 2012). The nascent ideal worker literature has focused on defining the norm, but the effects of the ideal worker on employees has been little studied. For example, gendered social roles, in which women are caretakers and men are breadwinners, may affect perceptions of female employees’ ability to fulfill the ideal worker norm, regardless of family context, negatively affecting their careers. Likewise, for men who deviate from this expectation, such as those who request flex time or parental leave (Sallee, 2012), they may be perceived as unable or unwilling to live up to the ideal worker norm. This paper symposium explores the effects of the ideal worker norm on the careers of male and female employees. Family-to-Work Conflict, Ideal Worker Norm Violation and Trust: Gender Bias in Evaluations Presenter: Jennica R. Webster; Marquette U. Presenter: Gary A Adams; Marquette U. Presenter: Andrea Schneider; Marquette U. Just Not Good Enough: How Supervisor Sexism Beliefs Affect Employee Promotability and Development Presenter: Clarissa Rene Steele; Kansas State U. Who are “Ideal Workers”? Intersection of Age, Gender, and Race Presenter: Vanessa Burke; Pennsylvania State U. Presenter: Alicia A. Grandey; Pennsylvania State U. Presenter: Teresa Frasca; Pennsylvania State U. Individual Worker and Managerial Characteristics that Influence Granting of Working from Home (WFH) Presenter: Alyson Gounden Rock; McGill U. - Desautels Faculty of Management “We Deserve Work-Life Balance Too”: Experiences of Dual-Career Professional Couples Without Children Presenter: Galina Boiarintseva; Niagara U. Presenter: Souha R. Ezzedeen; York U. Presenter: Julia Richardson; Curtin U. Presenter: Christa Wilken; York U. Getting Down to Brass Tacks: The Effect of Focusing on Results Instead of Ideal Worker Cues Presenter: Aurora Turek; -

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)0.021
metaresearch head score (Gemma)0.069
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 score0.021
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.069
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.005
Scholarly communication0.0040.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.012
GPT teacher head0.288
Teacher spread0.276 · 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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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