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Razão e sensibilidade no ensino de administração: a literatura como recurso estético

2007· article· pt· W2047769389 on OpenAlexaff
Tânia Fischer, Eduardo Davel, Sylvia Constant Vergara, Philip D. Ghadiri

Bibliographic record

VenueRevista de Administração Pública · 2007
Typearticle
Languagept
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsPhilosophyHumanities

Abstract

fetched live from OpenAlex

Este artigo problematiza o uso da literatura como recurso estético durante o processo de ensino da administração, com base em uma análise multidisciplinar dos trabalhos acadêmicos publicados, com a finalidade de: refletir sobre literatura e o papel da ficção no ensino, articulando a literatura com a existência humana; abordar a pesquisa em organizações e o uso de gêneros e estilos literários na produção do conhecimento; e discutir sobre os usos dos textos literários em ensino, pelo relato e discussão de práticas que utilizam a literatura. Tem especial destaque a dimensão estratégica da atividade de ensino que usa a literatura como recurso estético. A conclusão é de que o texto literário é um poderoso recurso de aprendizagem, pois tem como matéria-prima a palavra, o discurso, que é a essência da administração. E, também, que a integração entre administração e literatura pode ser uma estratégia fecunda, favorecendo criatividade e descoberta, pois possibilita o desenvolvimento de capacitações para sentir e conhecer.

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)0.019
metaresearch head score (Gemma)0.047
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: none
Teacher disagreement score0.041
Threshold uncertainty score0.118

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.047
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0090.012
Science and technology studies0.0100.036
Scholarly communication0.0410.032
Open science0.0030.011
Research integrity0.0070.007
Insufficient payload (model declined to judge)0.0100.002

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 head0.018
GPT teacher head0.268
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

Citations11
Published2007
Admission routes1
Has abstractyes

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