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Record W2177467116 · doi:10.7202/1033554ar

Using Inexpensive Technology and Multimedia to Improve Science Education in Rural Communities of Nepal

2015· article· en· W2177467116 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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueMcGill Journal of Education / Revue des sciences de l éducation de McGill · 2015
Typearticle
Languageen
FieldComputer Science
TopicICT in Developing Communities
Canadian institutionsMcGill University
Fundersnot available
KeywordsScience educationEducational resourcesPublic relationsBusinessPolitical scienceSociologyPedagogy

Abstract

fetched live from OpenAlex

This article explores an ongoing project that promotes science education in rural communities of western Nepal by using affordable technology. With the advent of inexpensive technology and multimedia resources, teaching materials for science education can be accessed with a much smaller budget than was previously possible. A preliminary survey done in two schools of Baglung district in Nepal found a significant lack of funding for science education. Using affordable computing technology such as Raspberry Pi and open-source electronic library contents, including those provided by Khan Academy and Wikipedia, this project will help foster the currently underutilized talent that exists in the country by making communities less dependent on external educational aid and hence promote ownership and progress of online educational platforms.

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)0.003
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.385
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0000.002
Open science0.0020.000
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 head0.306
GPT teacher head0.426
Teacher spread0.120 · 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