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Record W2046987398 · doi:10.5539/ibr.v8n3p11

Competency-Based Training Program for International Students

2015· article· en· W2046987398 on OpenAlexaffvenue
Ana Azevedo, Deborah Hurst, Rocky J. Dwyer

Bibliographic record

VenueInternational Business Research · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsAthabasca University
Fundersnot available
KeywordsCompetence (human resources)Experiential learningMindfulnessPsychologyFlexibility (engineering)Knowledge managementSustainabilityMedical educationEngineering ethicsPedagogyComputer scienceManagementEngineeringSocial psychology

Abstract

fetched live from OpenAlex

The purpose of this paper is to introduce a training program for developing the self-discovery and self-regulation competencies of international students. The paper combines literature review from global leadership, international education and related disciplines to advance a competency-based program that addresses a critical need for greater integration and retention of international students in their host countries. Research evidence from the literature review further suggested that key competencies such as self-awareness and self-regulation are critical for enabling the development of culturally intelligent behaviours. The program therefore introduces an initial phase of experiential learning activities in mindfulness and sense-making to foster the development of the foundational competence self-discovery. In addition, a second phase of entrepreneurial projects is designed to support individual growth in three self-regulation competencies: psychological flexibility, human sustainability and entrepreneurial thinking. The importance of this training program for the effective social integration and long-term retention of international students is discussed. Opportunities for future research are also outlined in the concluding section.

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)0.003
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 score0.010
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.003

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 head0.287
GPT teacher head0.530
Teacher spread0.243 · 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

Citations3
Published2015
Admission routes2
Has abstractyes

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