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Record W2133989833 · doi:10.5539/hes.v5n2p10

Perceptions of Multigrade Teaching: A Narrative Inquiry into the Voices of Stakeholders in Multigrade Contexts in Rural Zambia

2015· article· en· W2133989833 on OpenAlexvenueno aff
Charles Kivunja, Margaret Sims

Bibliographic record

VenueHigher Education Studies · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsIgnorancePovertyRural areaNarrativeCommitNarrative inquiryEconomic growthPedagogySociologyPsychologyPolitical scienceEconomics

Abstract

fetched live from OpenAlex

Multigrade teaching is used widely in primary schools throughout the Majority World. This study reports the findings of a narrative inquiry undertaken to answer the question: what are the perceptions of stakeholders in rural Zambian multigrade contexts about multigrade teaching as an education strategy? We were interested in exploring the reality of multigrade education. The inquiry found that although stakeholders in the main held positive perceptions about multigrade teaching, necessitated by lack of resources in rural areas, they saw it as a poor substitute for monograde teaching. The study confirms that the human and physical infrastructure in rural Zambia is in a poor state making unlikely the achievement of MDGs by 2015. Capacity building to enhance multigrade education is suggested as a possible strategy to accelerate the realisation of the MDG of Education for All (EFA) by 2015. Failure to provide effective multigrade teaching will commit millions of children, particularly in the rural areas in the Majority World, to the vicious cycle of extreme poverty, unemployment, hunger, ignorance and disease.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.982

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 studies0.0000.001
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 head0.222
GPT teacher head0.476
Teacher spread0.254 · 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 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

Citations22
Published2015
Admission routes1
Has abstractyes

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