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Record W2099144129 · doi:10.5430/ijhe.v4n1p254

The Political, Socio-economic and Sociocultural Impacts of the King Abdullah Scholarship Program (KASP) on Saudi Arabia

2015· article· en· W2099144129 on OpenAlexvenueno aff
Kholoud T. Hilal, Safiyyah R. Scott, Nina Maadad

Bibliographic record

VenueInternational Journal of Higher Education · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicSocioeconomic Development in MENA
Canadian institutionsnot available
Fundersnot available
KeywordsScholarshipPoliticsSociocultural evolutionPolitical scienceEconomic growthSociologyEconomicsLaw

Abstract

fetched live from OpenAlex

Since 2006, Saudi Arabian politicians, economists and sociologists have had to consider the implications of their country’s King Abdullah Scholarship Program (KASP). Because Saudi Arabia has certain religious traditions and economic practices that are sensitive, international scholars are examining from different perspectives the outcomes and potential impacts of KASP. While Saudi Arabia has all the necessary tools to compete with the developed nations (such as natural resources and manpower), it is caught between the need to globalise its economy but retain its strict, conservative traditions. Following a brief definition of KASP, this paper highlights some of the external and internal contemporary political, economic and socio-cultural challenges that it sets for Saudi Arabia. Finally, the anticipated impacts of KASP according to scholarship recipients are reviewed using survey data from around 688 overseas Saudi students. This paper is based on the primary researcher’s PhD thesis.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0030.001
Open science0.0000.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.040
GPT teacher head0.394
Teacher spread0.354 · 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 designQualitative
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

Citations37
Published2015
Admission routes1
Has abstractyes

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