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Record W4412844657 · doi:10.25071/28169344.142

A Critical Review of the Bologna Process to Draw Lessons for the Internationalization of Higher Education in Africa

2025· review· en· W4412844657 on OpenAlexaff
Yomni Makonnen Tesfaye

Bibliographic record

VenueYU-WRITE Journal of Graduate Student Research in Education · 2025
Typereview
Languageen
FieldSocial Sciences
TopicGlobal Educational Policies and Reforms
Canadian institutionsYork University
Fundersnot available
KeywordsInternationalizationBologna ProcessPolitical scienceProcess (computing)Higher educationBusinessComputer scienceInternational tradeLaw

Abstract

fetched live from OpenAlex

With Globalization and the increasing commodification of education has pushed governments and universities worldwide to boost their competitiveness, while simultaneously promoting international cooperation (Piro, 2016). The Bologna Process, launched in 1999, is a large-scale initiative that works to harmonize European higher education, promoting mobility within the region and advancing Europe as a knowledge-hub (European Commission, n.d.). While primarily concerned with Europe, the Bologna Process has had global influence. Some African education systems have adopted the process despite unique challenges such as resource limitations, colonial histories, and political instability (Alemu, 2018). This essay critically reflects on the Bologna Process, its impact on higher education in Africa, and explores how Africa can learn from it to improve its higher education landscape in sustainable and contextually relevant ways. I argue that while the Bologna Process promotes mobility and international cooperation, it is unsuitable for the African context, where it becomes a form of soft power, reinforcing European hegemony (Charlier & Croché, 2011). African institutions require internationalization strategies tailored to their historical, cultural and socio-political context to ensure that education serves Africa’s internal needs and goals.

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 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)0.006
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.808
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.334
GPT teacher head0.632
Teacher spread0.298 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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
Published2025
Admission routes1
Has abstractyes

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