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Beyond macro-indicators: Exploring micro-level educational experiences (MLEs) reinforcing learning inequality in rural northern Ghana

2025· article· en· W4417408091 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInternational Journal of Educational Development · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Educational Policies and Reforms
Canadian institutionsMcGill University
FundersFonds de Recherche du Québec-Société et CultureInternational Development Research Centre
KeywordsDisadvantageScholarshipInequalityPsychological interventionEthnic groupRural areaEducational inequalityQualitative researchDisadvantaged

Abstract

fetched live from OpenAlex

Educational interventions in Ghana and Sub-Saharan Africa (SSA) have, in recent decades, primarily focused on improving macro-level indicators around enrolment, attendance, completion, and learning outcomes. Existing scholarship pays limited attention to understanding the structural and socio-economic disadvantages in different geolocations that shape children’s schooling and learning. Drawing on historical accounts of disadvantage in northern Ghana, this study examines how local environment experiences in rural northern communities constrain children’s access to schooling and learning, using ecological theory to frame these complex influences. Through qualitative interviews and focus groups with key local education stakeholders, we demonstrate how children’s interactions with their temporal and policy environments generate micro-level educational experiences (MLEs) that reinforce schooling and learning exclusion in rural northern communities – outcomes that risk widening the inequality gap between rural northern schools and the rest of Ghana. The findings point to tensions between the formal school system and the temporal lifestyle of rural communities, persistent insecurity linked to tribal and ethnic conflicts, complications with the language-of-instruction policy, and shortages of teaching and learning materials (TLMs) as MLEs that foster learning alienation. We argue that Ghana’s ambition to achieve quality and equitable basic education and learning skills for all children by 2030 requires far more than universalizing enrolment. Achieving sustainable progress depends on targeted policy interventions that address MLEs embedded within the broader structural and socio-economic realities of rural northern communities, ensuring that education systems align – rather than conflict with children’s lived environments.

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.

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)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.125
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.349
Teacher spread0.321 · 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