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Record W2799996837 · doi:10.5430/ijhe.v7n3p36

The Capabilities Approach: A Future Alternative to Neoliberal Higher Education in the MENA Region

2018· article· en· W2799996837 on OpenAlexvenueno aff
Ranya Elkhayat

Bibliographic record

VenueInternational Journal of Higher Education · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Educational Policies and Reforms
Canadian institutionsnot available
Fundersnot available
KeywordsCommodificationIdeologyState (computer science)Neoliberalism (international relations)Higher educationDiversity (politics)Political scienceGlobal educationScale (ratio)SociologyEconomic growthPolitical economyPedagogyEconomicsEconomyPoliticsLawGeographyComputer science

Abstract

fetched live from OpenAlex

This conceptual paper is a future study aiming to shed light on the current state of higher education in the MENA region. The neoliberal agenda for higher education in the region presents a form of education that is commodified, corporatized and focused on STEM rather than on humanities. The paper further speculates on the state of higher education in the near future under the same ideology. As an alternative, the study proposes the implementation of Martha Nussbaum’s Capabilities Approach. This approach is capable of re-balancing the tipped scale in the commodification of higher education and will serve in developing well-rounded individuals. The Capabilities Approach can reform higher education through critical thinking, liberal education, and attention to diversity.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.016
Scholarly communication0.0060.007
Open science0.0010.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.019
GPT teacher head0.363
Teacher spread0.344 · 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 designTheoretical or conceptual
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

Citations5
Published2018
Admission routes1
Has abstractyes

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