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A INFLUÊNCIA DA LIDERANÇA COMO ESTÍMULO À MOTIVAÇÃO DO TRABALHO EMEQUIPE

2018· article· en· W2921978588 on OpenAlexaff
Cláudio José Donato, Eduardo de Lima Silva, Hualacy Guilherme Odilon do Nascimento, Irene Caires da Silva, Joselene Lopes Alvim, Leticia Moreira da Silva, Liége Xavier Martins, Lucas de Souza Miranda, Maísa Ferreira Vieira, Tais Muller, Tatiana Veiga Uzeloto, Vinicius da silva Soares

Bibliographic record

VenueCOLLOQUIUM SOCIALIS · 2018
Typearticle
Languageen
FieldDecision Sciences
TopicBusiness and Management Studies
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsTeamworkPsychologySociologyPublic relationsManagementPolitical science

Abstract

fetched live from OpenAlex

Considering that the main challenge of leadership today is to retain and develop the human capital of organizations, we seek to show the influence of leadership as a stimulus to motivation for teamwork. Thus, this article addresses one of the most researched and studied themes in recent years: leadership and motivation. Today leadership has been considered an essential tool for the success of organizations, emphasizing, mainly, the role that it exerts on human motivation for teamwork. Methodologically, qualitative, descriptive, and bibliographic research was utilized through scientific articles, books, and specialized websites. Based on the research carried out it was realized that the leader is of extreme importance to any type of organization or company in order to promote the motivation, sustainability and even more the development of organizations and or groups that acts or participates.

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)0.008
metaresearch head score (Gemma)0.048
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.048
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.181
GPT teacher head0.431
Teacher spread0.250 · 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 designQualitative
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

Citations0
Published2018
Admission routes1
Has abstractyes

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