Everyday objects of learning about health and healing and implications for science education
Bibliographic record
Abstract
Abstract The role of science education in rural development is of great interest to science educators. In this study I investigated how residents of rural Kirumi, Kenya, approach health and healing, through discussions and semistructured and in‐depth interviews with 150 residents, 3 local herbalists, and 2 medical researchers over a period of 6 months. I constructed objects of learning by looking for similarities and differences within interpretive themes. Objects of learning found comprise four types of personal learning tools, three types of relational learning tools, three genres of moral obligation, and five genres of knowledge guarding. Findings show that rural people use (among other learning tools) inner sensing to engage thought processes that lead to health and healing knowledge. The sociocultural context is also an important component in learning. Inner sensing and residents' sociocultural context are not presently emphasized in Kenyan science teaching. I discuss the potential use of rural objects of learning in school science, with specific reference to a health topic in the Kenyan science curriculum. In addition, the findings add to the literature in the Science, Technology, Society, and Environment (STSE) approach to science education, and cross‐cultural and global science education. © 2005 Wiley Periodicals, Inc. J Res Sci Teach 43: 172–193, 2006
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".