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Record W2998511892 · doi:10.5430/ijhe.v9n2p1

International Partnerships for the Development of STEM and Medical Education of Middle Eastern Women

2019· article· en· W2998511892 on OpenAlex
Daniel Wang, David P. Hajjar, Curtis L. Cole

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternational Journal of Higher Education · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSocioeconomic Development in MENA
Canadian institutionsnot available
Fundersnot available
KeywordsMiddle EastGraduation (instrument)Face (sociological concept)Political scienceEconomic growthSustainable developmentSociologyEngineeringSocial scienceEconomics

Abstract

fetched live from OpenAlex

The development of a knowledge economy requires a nation to foster a robust foundation in science, technology, engineering, and mathematics (STEM). The countries of the Arab world, including the oil-rich nations of the Gulf, have made great strides in this regard, though much remains to be done. This article examines the cultural barriers these nations face, such as a lack of educational access and post-graduation employment opportunities for women, as well as systematic obstacles, such as poor quality of STEM education. The article also examines international partnerships with foreign institutions as viable solutions for the Arab world, with the case of Weill Cornell Medicine-Qatar presented as a case study of a sustainable, mutually beneficial endeavor.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.823
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.058
GPT teacher head0.366
Teacher spread0.308 · 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