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Record W4390918843 · doi:10.1037/xge0001516

Interdependent behavior only benefits employees from working-class backgrounds when it is both enacted and valued.

2024· article· en· W4390918843 on OpenAlexaff
Andrea Dittmann, Nicole K. Stephens, Sarah S. M. Townsend

Bibliographic record

VenueJournal of Experimental Psychology General · 2024
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsKellogg's (Canada)
FundersTempleton World Charity Foundation
KeywordsInterdependencePsycINFOSocial psychologyWorking classPsychologyClass (philosophy)SociologyPolitical scienceComputer science

Abstract

fetched live from OpenAlex

= 2,566), we find that they do not. We theorize and document that this is because there is often a decoupling between enacting interdependent behavior and whether such behavior is valued as part of being a "good" employee. We find that employees from working-class backgrounds only experience a cultural match and its benefits (e.g., sense of fit, high retention intentions) when interdependent behaviors are both enacted and valued. In contrast, when interdependent behaviors are enacted but not valued, employees from working-class backgrounds experience a cultural mismatch. Furthermore, we find that this pattern is unique to employees from working-class backgrounds: Employees from middle-class backgrounds report similar fit and retention regardless of whether there is a coupling of enacted and valued interdependent behavior. Taken together, our results suggest that it is critical to examine multiple elements of culture simultaneously (e.g., both enacted and valued behavior) to fully understand and predict the consequences of cultural (mis)match. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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)0.001
metaresearch head score (Gemma)0.005
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 score0.010
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.117
GPT teacher head0.415
Teacher spread0.298 · 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

Citations7
Published2024
Admission routes1
Has abstractyes

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