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Record W7132904326

Factors Affecting Orphans and Vulnerable Children Realizing their Right to Education in Mozambique

2022· dissertation· W7132904326 on OpenAlexaff
Natasha Richmond

Bibliographic record

VenueTSpace · 2022
Typedissertation
Language
FieldSocial Sciences
TopicPoverty, Education, and Child Welfare
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFace (sociological concept)Right to educationMillennium Development GoalsDeveloping countryPovertySustainable developmentAffect (linguistics)
DOInot available

Abstract

fetched live from OpenAlex

Despite notable efforts from countries and non-governmental organizations (NGOs) around the world, the right to education among orphans and vulnerable children (OVCs) in Sub-Saharan Africa is far from realized. Despite an increase in the number of girls in school over the past few decades, we cannot overlook the failures and growing disparities faced by the poorest and most vulnerable children. OVCs continue to suffer and face unfair educational access and opportunities despite initiatives by governments, development agencies, international organizations, NGOs, Education for All, Millennium Development Goals, and their successive Sustainable Development Goals. NGOs play a significant role in the educator sector of most African countries. This thesis focuses on the local response of an NGO in Mozambique and seeks to answer: What factors affect (hinder or enhance) OVCs realizing their right to education with a special focus on the female OVCs among them?

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.156
Threshold uncertainty score0.311

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.014
GPT teacher head0.344
Teacher spread0.330 · 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 designQualitative
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
Published2022
Admission routes1
Has abstractyes

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