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Record W4392598294 · doi:10.3389/feduc.2024.1336576

The social innovation profile in students as a transformation strategy: structural equation modeling

2024· article· en· W4392598294 on OpenAlexaff
Leonardo David Glasserman‐Morales, Carolina Alcantar-Nieblas, Sergio Nava-Lara

Bibliographic record

VenueFrontiers in Education · 2024
Typearticle
Languageen
FieldComputer Science
TopicEducational Innovations and Challenges
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsStructural equation modelingTransformation (genetics)Social innovationComputer scienceKnowledge managementIndustrial engineeringEngineeringPolitical sciencePublic relationsChemistry

Abstract

fetched live from OpenAlex

The development of social entrepreneurship competencies in university students favors the generation of proposals for solutions to different social problems, thus promoting a positive social impact capable of transforming people’s living conditions. This study aims to analyze the relationship between the dimensions of social entrepreneurship competence: entrepreneurial management, social value, leadership, effective communication and social innovation. A total of 408 higher education students from 13 countries and six different disciplinary areas participated in the study, ranging in age from 18 to 58 years ( M = 22.4, SD = 6.0). The social entrepreneurship instrument was used, which is composed of 28 items grouped into five dimensions. A structural equation model was calculated. The findings indicate that entrepreneurial management, social value, and leadership are directly and positively related to personal competencies and social innovation. These results prove the importance of social entrepreneurship training in the development of social innovation in students, it is important that educational institutions in general carry out a review of their curricula and programs that take into account the development of social innovation competence as a factor that can enhance social change.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.774
Threshold uncertainty score0.489

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.038
GPT teacher head0.372
Teacher spread0.334 · 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 teacher head, 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

Citations3
Published2024
Admission routes1
Has abstractyes

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