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Record W4408591661 · doi:10.1177/14788047251327548

Teaching self-reliance and empowerment in civics classrooms: Towards a sociopolitical capabilities approach

2025· article· en· W4408591661 on OpenAlexaff
Adaobiagu Obiagu

Bibliographic record

VenueCitizenship Social and Economics Education · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicPoverty, Education, and Child Welfare
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCivicsEmpowermentMathematics educationSociologyPedagogyPsychologyEconomic growthEconomics

Abstract

fetched live from OpenAlex

This paper considers whether self-reliance and empowerment discourses within Nigerian civic education are sufficient to actualise civic education aims of promoting freedom, human agency, and active citizenship. Drawing on qualitative data, it analysed the representations of self-reliance and youth empowerment concepts in civic education curriculum and prescribed textbooks for secondary schools, along with six civics teachers’ understanding and classroom implementation of these concepts. Findings show that economic-centric/neoliberal perspective dominates the framing of these concepts within civic education. This dominant perspective is driven by how self-reliance is framed in Nigeria's education policy. Relying on human capability and sociopolitical development models, the paper argues that the neoliberal perspective framing of self-reliance and empowerment undermines actualising the goals of civic education: to empower citizens for civic agency and sociopolitical actions that challenge systemic issues, such as inequality and corruption, which perpetuate social injustices like unemployment and poverty targeted by economic empowerment. Noting an increase in poverty and unemployment targeted by human capital approaches, the paper recommends a shift towards sociopolitical capabilities-informed perspective on self-reliance and empowerment in civic education. This, it argues, will complement the economic-centric perspectives emphasised in vocational subjects and foster holistic human flourishing.

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 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: Empirical
Teacher disagreement score0.351
Threshold uncertainty score0.763

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 studies0.0010.000
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 head0.017
GPT teacher head0.294
Teacher spread0.277 · 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 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

Citations1
Published2025
Admission routes1
Has abstractyes

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