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Record W2104380068 · doi:10.20355/c54g6n

The iSPACES Framework for Rethinking a Culturally Responsive Secondary Science Curriculum in Tanzania

2014· article· en· W2104380068 on OpenAlexvenueno aff
Ladislaus M. Semali

Bibliographic record

VenueJournal of Contemporary Issues in Education · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicScience Education and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumTanzaniaRestructuringScience educationMathematics educationPedagogySociologyEngineering ethicsEngineeringPsychologyPolitical science

Abstract

fetched live from OpenAlex

Abstract The iSPACES project for teaching a culturally responsive science curriculum in Tanzania emphasizes practical skills to develop scientific knowledge among secondary school students. iSPACES employs a framework that involves interdisciplinary teaching to motivate students to study science, technology, engineering and mathematics (STEM) and to produce useful products that will fill needs encountered in real life. This discussion considers methods for restructuring an existing curriculum and rethinking the methodologies for teaching of physics, chemistry and biology (PCB) to overcome students’ cognitive conflicts between their everyday world and the world of academic science. The examination concludes with an example of a framework for a chemistry lesson that may guide teachers who wish to rethink PCB pedagogy and designers who wish to create culturally responsive curricula.

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)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.005
Scholarly communication0.0040.002
Open science0.0020.005
Research integrity0.0010.002
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 head0.037
GPT teacher head0.423
Teacher spread0.386 · 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 designTheoretical or conceptual
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
Published2014
Admission routes1
Has abstractyes

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