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Record W2201497187 · doi:10.59588/2243-786x.1315

South Koreans Studying in DLSU-Manila: Challenges and Opportunities in Trade in Education Services

2013· article· en· W2201497187 on OpenAlexaboutno aff
Luz T. Suplico-Jeong, Rechel Arcilla

Bibliographic record

VenueDLSU Business & Economics Review · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsnot available
Fundersnot available
KeywordsQuality (philosophy)English languageNiche marketBusinessEconomic growthMarketingPolitical scienceInternational tradeEconomicsPsychology

Abstract

fetched live from OpenAlex

Trade in education services plays a crucial role in economies like Australia, Canada, New Zealand, UK, and USA. The largest component of trade in education services consists of students who travel abroad to study. This market is going to grow as international student mobility continues to increase. The Philippines has been a popular destination for South Korean students who want to study abroad because English is widely spoken. It offers the same quality of English education at a lower cost. This paper examines the economic contribution and challenges of the South Korean students in the Philippines. A marketing strategy to enhance this niche market includes promoting the Philippines as an ESL (English as a Second Language) destination toward the Korean families as families play crucial role in deciding which overseas schools to study. Further, this strategy should stress the Philippine advantage of having English education at a lower cost.

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)0.001
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 score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.105
GPT teacher head0.298
Teacher spread0.193 · 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
Published2013
Admission routes1
Has abstractyes

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