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Record W4407387371 · doi:10.1080/10564934.2025.2454684

Neoliberal Repercussions in Postsecondary Education Sector: A Comparative Case Study Between Uzbekistan and Bangladesh with Emphasis on Equity, Diversity, and Inclusion

2024· article· en· W4407387371 on OpenAlexaff
Dilsora Fozilova, Mustahid Husain

Bibliographic record

VenueEuropean Education · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicPolitics and Conflicts in Afghanistan, Pakistan, and Middle East
Canadian institutionsUniversity of TorontoUniversity of the Fraser Valley
Fundersnot available
KeywordsInclusion (mineral)Equity (law)Diversity (politics)Economic growthPolitical scienceWater sectorPostsecondary educationSociologyHigher educationSocial scienceEconomicsBiology

Abstract

fetched live from OpenAlex

This analytical article problematizes the observable similar phenomenon faced by the postsecondary education sector in Uzbekistan and Bangladesh, in particular, the outcomes on the nations’ social science education vis-à-vis equity, diversity, and inclusion (EDI). Both countries started implementing neoliberal policies since the beginning of the 1990s through budget cuts in social science and through privatization employing market development advisory. The similarities highlighted by the phenomenon appear interesting despite the two countries’ distinct differences in geography, culture, climate, demography, and history, among others. First, we assess the market development policies in higher education in relation to EDI practices. In both countries, the needs of the students with disability and other forms of impairment are acute. The expectations of the religious minority and gender diverse students remain largely nonmaterialized. We critique a tendency in both countries toward picking and choosing market development policies that favor university administrators and owners. The observed outcomes, unfortunately, appear to weaken higher education objectives, in particular EDI. In addition, these outcomes subtly discourage students pursuing social change and market EDI philosophy, concepts, and theories as unattractive, nonprofitable pursuits. We argue that the outcomes can produce zombie graduates who remain unable to contribute to the broader debates in inclusivity and critical thinking. The outcomes can also result in fragmented, fragile scholars, practitioners, and members of civil society, who feel discouraged to produce cutting edge EDI research and scholarship. The paucity in the EDI phenomenon in Uzbekistan and Bangladesh thrives on dual-hegemonic and client–patron relations that undermine intellectual and political diversity. Our findings on the credentials and the quality of dissertations and publications produced by the academics in the countries’ social science sector, in comparison with benchmark scholars and their institutions, form a depressing yet similar pattern. We also came across nonexistence of ethical practices in the domain of social research, as well as unscrupulous practices in research methodology and scholarly publishing platforms, such as Scopus and Cambridge Scholars. The phenomenon complements academic censorship and a culture of fear that appears to envelop social science research in Uzbekistan and Bangladesh. Often, the persecution of scholars and practitioners with a different political view also appears to sustain a climate of apprehension. This social environment of fear extends to the parents of students. Many students remain understandably reluctant to engage in research or discussion on gender diversity and politically sensitive subjects. Consequently, students often choose safer, noncontroversial research topics, for example, for the bachelor’s in business administration, which may increase placement opportunities but stifle intellectual growth and critical thinking. At the same time, educators often avoid certain topics in the classroom, such as transgender rights, thus depriving students of a holistic education that addresses inclusivity and critical social and political issues.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.327
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.073
GPT teacher head0.388
Teacher spread0.315 · 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.

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

Citations2
Published2024
Admission routes1
Has abstractyes

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