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Record W7106818636 · doi:10.1080/03075079.2025.2592849

From scholars to militants: university leadership and the demographics of senior administrators in Taliban-ruled Afghanistan

2025· article· en· W7106818636 on OpenAlexaff

Bibliographic record

VenueStudies in Higher Education · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicPolitics and Conflicts in Afghanistan, Pakistan, and Middle East
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDemographicsHigher educationQualitative researchEducational leadershipSemi-structured interviewFocus group

Abstract

fetched live from OpenAlex

This article examines how the Taliban regime has reshaped universities’ administration in Afghanistan. Using a mixed-methods approach, the study analyzed data from 160 administrators across 40 universities and conducted thematic analysis of 30 biographies to explore their professional and ideological profiles. Findings reveal a systematic replacement of experienced, diverse, and academically qualified administrators with ideologically loyal and predominantly Pashtun men primarily educated in Pakistani madrasas, many of them involved in the Taliban insurgency. This is a clear shift away from merit-based academic administration and toward appointments based on madrasa education, ethnic ties, and ideological allegiance, and raises questions about the nature of universities and the conditions under which they cease to function as institutions of higher education in authoritarian and extremist contexts. The article argues that, under Taliban rule, universities have been transformed, from spaces of critical inquiry, into instruments of ideological reproduction.

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0050.003
Scholarly communication0.0040.002
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.094
GPT teacher head0.386
Teacher spread0.292 · 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 designObservational
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

Citations1
Published2025
Admission routes1
Has abstractyes

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