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Record W4413161245 · doi:10.24908/ijesjp.v12i1.18988

Envisioning the future of engineering education through Africanfuturism

2025· article· en· W4413161245 on OpenAlexvenueno aff
Earl E. Lee, Nadia Kellam

Bibliographic record

VenueInternational Journal of Engineering Social Justice and Peace · 2025
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsnot available
Fundersnot available
KeywordsEngineering ethicsEngineering educationEngineeringEngineering managementArchitectural engineering

Abstract

fetched live from OpenAlex

This paper explores how Africanfuturism, specifically Nnedi Okorafor’s Binti series, offers a transformative framework for reimagining engineering education to promote inclusivity, belonging, and diverse epistemologies. Grounded in Marcus Garvey’s call to honor cultural histories, we critique the colonial legacies of engineering, which favor Western knowledge systems while sidelining non-Western contributions. Drawing from bell hooks’ concept of transformative education, we argue that Africanfuturism challenges exclusionary practices by incorporating ancestral knowledge, cultural traditions, and liberatory visions into STEMM (science, technology, engineering, mathematics, and medicine) fields. Through thematic analyses of race and prejudice, innovation rooted in tradition, and cultural identity, Binti illustrates how Black epistemologies—grounded in community, spirituality, and historical consciousness—can reshape engineering education. We emphasize the potential of Africanfuturist narratives to decenter whiteness, confront systemic racism, and foster educational environments where marginalized students can thrive. By centering Black radical imagination, this paper advocates for an interdisciplinary, justice-oriented approach that integrates storytelling, cultural identity, and diverse ways of knowing to cultivate engineers committed to equity, empathy, and social transformation. Updated with minor typographical corrections: June 30, 2025.

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)0.000
metaresearch head score (Gemma)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.739
Threshold uncertainty score0.403

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.004
GPT teacher head0.247
Teacher spread0.243 · 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
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

Citations0
Published2025
Admission routes1
Has abstractyes

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