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Record W4394400189 · doi:10.6084/m9.figshare.21907588

Information and entrepreneurship: a case study with Brazilian and Canadian academic

2023· dataset· en· W4394400189 on OpenAlexaboutno aff
Flavia Fonseca, Mônica Érichsen Nassif

Bibliographic record

VenueFigshare · 2023
Typedataset
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Capital and Performance Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsEntrepreneurshipLibrary sciencePolitical scienceGeographyRegional scienceComputer scienceLaw

Abstract

fetched live from OpenAlex

ABSTRACT This article brings information and academic entrepreneurship as central themes, researched in the light of the Biology of Knowing. It aims to identify where is located the information that supports the entrepreneurial mindset of academics at the Federal University of Minas Gerais (UFMG), in Brazil, and Western University, in Canada, characterizing the family and academic environments to which they are exposed. Based on two case studies, the research has a qualitative and descriptive characteristic, and data were collected from structured interviews that considered the life stories of seven UFMG professors and seven Western University alumni. The primary evidence is that the determinant information of the entrepreneurial mentality was built from two factors in interaction: the biological characteristics of the research subjects, open to the entrepreneurial initiative, and the primary influence of the family environment, whether through life experiences or memories of words and attitudes of parents and relatives. The academic environment was also relevant, being able to encourage or act as a barrier to entrepreneurship. The relevance of entrepreneurial education is highlighted, fundamental for the formation of the knowing and decision-making subject, in constant interaction with other subjects and environments.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.350
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0140.009

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 head0.026
GPT teacher head0.240
Teacher spread0.214 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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
Published2023
Admission routes1
Has abstractyes

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