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Record W4321786350 · doi:10.5430/jct.v12n1p310

Generic Competences in Peruvian University Students

2023· article· en· W4321786350 on OpenAlexvenueno aff
Miguel A. Saavedra-López, Xiomara M. Calle-Ramírez, Ronald M. Hernández, Javier A. López-Céspedes, Jose Luis Saly Rosas Solano, Karen Pérez Maraví, Jacqueline C. Ponce-Meza

Bibliographic record

VenueJournal of Curriculum and Teaching · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEducational Research and Science Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsPublic universityHigher educationMedical educationInterpersonal communicationPsychologyCompetence (human resources)Sample (material)Mathematics educationPedagogyMedicinePolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

In higher education, generic competencies are those common to most students that can be used in the activities of a sector or organization. The objective was to identify generic competencies in Peruvian university students. The study was a descriptive, non-experimental cross-sectional design. The sample consisted of 567 university students of both sexes from public and private universities in Peru. The instrument applied was the questionnaire of generic competencies of university students (CCGEU). The results showed that Peruvian university students have a sufficient level of generic competencies; they use generic competencies almost always or always in the development of their activities. Likewise, in the dimensions, systemic competencies have the highest value, followed by interpersonal and instrumental competencies. It is concluded that most Peruvian university students use generic competencies at a sufficient level; however, some students never or almost never use them, so it would be important for the Peruvian university institutions where the participants study to implement strategies to strengthen generic competencies.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.031
GPT teacher head0.297
Teacher spread0.267 · 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 designObservational
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
Published2023
Admission routes1
Has abstractyes

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