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Record W2564996957 · doi:10.1111/jasp.12421

Receipt of interpersonal citizenship: fostering agentic emotion, cognition, and action in organizations

2016· article· en· W2564996957 on OpenAlexaff
Dana Kabat‐Farr, Lilia M. Cortina

Bibliographic record

VenueJournal of Applied Social Psychology · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsDalhousie University
FundersHorace H. Rackham School of Graduate Studies, University of MichiganInstitute for Research on Women and Gender, University of MichiganUniversity of Michigan
KeywordsReceiptPsychologyInterpersonal communicationSocial psychologyWorkforceAction (physics)Task (project management)CitizenshipEmpowermentTest (biology)Applied psychologyPublic relationsManagementPolitical science

Abstract

fetched live from OpenAlex

Abstract With an eye to fostering an energized and empowered workforce, we explore the discrete emotion of self‐assurance (characterized by boldness, pride, and audacity), investigating how receipt of interpersonal citizenship behavior (ICB ) fuels this agentic emotion. ICB includes acts of everyday concern that may be of a person‐ or task‐focused nature. With two survey samples, we propose and test a model that situates self‐assurance as a mechanism linking ICB‐receipt to employee thriving and empowerment. Additionally, we find links to citizenship enactment, as reported by coworkers. Notably, person‐focused ICB‐receipt may be just as beneficial to self‐assurance as task‐focused ICB‐receipt. These results hold equally for working women and men. Our multi‐study, multi‐source results underscore the role of agentic emotion in cultivating a proactive workforce.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.667
Threshold uncertainty score0.608

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.305
Teacher spread0.265 · 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 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

Citations32
Published2016
Admission routes1
Has abstractyes

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