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Record W4390437027 · doi:10.37811/cl_rcm.v7i6.8910

Impacto de la Movilidad Estudiantil Internacional en el Desarrollo de Habilidades del Egresado de la Licenciatura en Idiomas

2023· article· es· W4390437027 on OpenAlexaff
Ernesto De los Santos García, Eleazar Morales Vázquez, Irma Alejandra Coeto Calcáneo

Bibliographic record

VenueCiencia Latina Revista Científica Multidisciplinar · 2023
Typearticle
Languagees
FieldSocial Sciences
TopicHigher Education and Sustainability
Canadian institutionsImpact
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

El mundo se encuentra en constante cambio e intercambio cultural, por lo que los seres humanos deben estar preparados para vivir y convivir en entornos cambiantes y heterogéneos. Una de las principales estrategias del sector educativo para esto es la internacionalización a través de la movilidad estudiantil. Por esta razón, en este artículo se presentan los resultados de un estudio implementado con egresados de la Licenciatura en Idiomas de la Universidad Juárez Autónoma de Tabasco que realizaron movilidad estudiantil internacional, para determinar el impacto que esta tuvo en el ámbito profesional de los egresados. Los resultados permitieron concluir que la movilidad estudiantil internacional genera diferentes beneficios para los egresados de la Licenciatura en Idiomas tanto a nivel profesional como personal. Esta experiencia ayuda a mejorar el desempeño, ya que promueve el desenvolvimiento, la adaptabilidad, la resolución de problemas, así como brinda la posibilidad de mejorar las habilidades comunicativas tanto en la lengua materna como en una segunda lengua o lengua extranjera.

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)0.005
metaresearch head score (Gemma)0.009
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0050.003
Scholarly communication0.0050.001
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.001

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 head0.011
GPT teacher head0.377
Teacher spread0.366 · 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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