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Record W7064626802

Changing educational aspirations of children living in poverty in Ethiopia

2010· article· en· W7064626802 on OpenAlexfundno aff

Bibliographic record

VenueOxford University Research Archive (ORA) (University of Oxford) · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
FundersDepartment for International DevelopmentInternational Development Research CentreBernard van Leer FoundationUniversity of OxfordInter-American Development Bank
KeywordsPovertyLivelihoodGovernment (linguistics)Compulsory educationRural areaWork (physics)Educational attainmentRealisationLiteracyBasic education
DOInot available

Abstract

fetched live from OpenAlex

Using both qualitative and quantitative data, this paper examines the changing educational and occupational aspirations and educational achievements of children living in poor communities in Ethiopia. The results suggest that children had high aspirations at an earlier age but that these changed later, with poverty rarely influencing their earlier aspirations but having a strong impact later on. Children with high educational achievement, mostly urban children and some rural girls, maintained their high ambitions. Education policy imposed constraints and provided varied opportunities for rural and urban children that affected their educational achievement and aspirations. Educational achievement was influenced by age of entry to school and continued attendance. Government development programmes and agricultural livelihoods attracted rural children’s labour and thereby negatively affected their education and realisation of their ambitions. The longitudinal data suggest that some children have begun considering out-of-school transitions (e.g. girls’ early marriage and full-time work for girls and boys) and, as a consequence, it seems that very few poor children will be able to realise their ambitions.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.152
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
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0250.000

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 head0.018
GPT teacher head0.257
Teacher spread0.239 · 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 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

Citations3
Published2010
Admission routes1
Has abstractyes

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