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Diseño y validación de instrumento para medir el emprendimiento corporativo en Costa Rica

2024· article· en· W4403019746 on OpenAlexaff
Cris Bravo Monge

Bibliographic record

VenueEstudios Gerenciales · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBusiness, Innovation, and Economy
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

This study presents the development, validation, and application of an instrument tailored to assess the conduciveness of the internal environment for corporate entrepreneurship among large Costa Rican organizations. Based on a sample of 201 managers, principal component analysis was used to identify and validate a new measuring tool. The validated instrument was applied to a sample of 184 managers belonging to four different economic sectors. T-tests and ANOVA were implementedto identify differences in the perceptions of participants. The results of the application of the instrument suggest that while perceptions of conduciveness of the internal environments significantly differ among sectors, the lack of time and incentives for innovation is negatively associated with the entrepreneurial climate among all large firms in Costa Rica.

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)0.034
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.182

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0340.037
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
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 head0.051
GPT teacher head0.266
Teacher spread0.215 · 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 designBench or experimental
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

Citations2
Published2024
Admission routes1
Has abstractyes

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