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Record W2947752131 · doi:10.5539/ass.v15n6p88

(Im)Possibility of Learning Science Through Livelihood Activities at Community Schools in Nepal

2019· article· en· W2947752131 on OpenAlexvenueno aff
Kamal Prasad Acharya, Rajani Rajbhandary, Milan Acharya

Bibliographic record

VenueAsian Social Science · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Educational Policies and Reforms
Canadian institutionsnot available
FundersUniversity Grants Commission
KeywordsLivelihoodCurriculumPedagogyPerspective (graphical)Set (abstract data type)SociologyCommissionPopulationPolitical scienceMedical educationMathematics educationPsychologyGeographyMedicine

Abstract

fetched live from OpenAlex

The Science in the Learning Home (SciLH) program was designed to address two well-documented, inter-related educational problems observed in the Community High Schools in Nepal. The first relates to the achievement of students in science in Secondary Education Examination (SEE), which is below average (33 out of 75 i.e. 44%), and the second concerns the insufficiencies of the resources and instruction to discourse their traditional and livelihood requirements through school science learning activities. Funded by the University Grants Commission (UGC), Nepal, as a Small Research Development and Innovation Grants (SRDIG) to the faculty member, SciLH is a title set by the researcher to provide a new and innovative concept to learn science from the home and cultural practices. The tenth-grade high school students and the community people (parents) participated in the study. Livelihood practices and activities at the home link SciLH concept aligns with the school science curriculum and textbooks with that of cultural practices. This research article offers a framework to explore factors which support the accomplishment of the ethnically different student population and parents using the outline of ethno-perspective.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.352
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0040.007
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.021
GPT teacher head0.353
Teacher spread0.332 · 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; both teacher heads agree on what is shown here.

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

Citations12
Published2019
Admission routes1
Has abstractyes

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