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Record W1023209909 · doi:10.1016/s2212-5671(15)00394-9

Intermediate Manager Behaviors Leading to Employee Job Performance and Well-being: A Conceptual Framework

2015· article· en· W1023209909 on OpenAlexaff
Julianna Marcotte, Olivier Doucet, Michel Cossette

Bibliographic record

VenueProcedia Economics and Finance · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsCoachingConceptual frameworkKnowledge managementField (mathematics)PsychologyKey (lock)Focus (optics)Human resource managementPublic relationsBusinessComputer sciencePolitical scienceSociology

Abstract

fetched live from OpenAlex

Over the past century, the subject of key management behaviors leading to employee job performance and well-being has generated great interest in the scientific and professional community. In the literature, we observe that those decisive managerial behaviors have been the focus of many studies in three major sub-fields: operational management, leadership, and managerial coaching. Although, research in each field have contributed to our understanding of the efficacy of such management behaviors, a comprehensive picture is yet to develop, as they have evolved independently from one another. The purpose of this study is thus to reconciliate these three distinct bodies of literature and to synthesize the main theories underlying, creating an innovative framework to further our understanding of key management behaviors.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0060.003
Science and technology studies0.0020.008
Scholarly communication0.0060.004
Open science0.0020.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.013
GPT teacher head0.219
Teacher spread0.206 · 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 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

Citations1
Published2015
Admission routes1
Has abstractyes

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