The Humanistic and Cultural Aspects of Science & Technology Education
Bibliographic record
Abstract
Glen Aikenhead, Ph.D., Professor of Education, University of Saskatchewan, Saskatoon, Canada, is the author of Science Education for Everyday Life Evidence-Based Practice. His work provides a comprehensive overview of international research on humanistic approaches to teaching science―approaches that connect students to broader human concerns in their everyday life and culture. Glen Aikenhead, an expert in the field of culturally sensitive education, summarizes major worldwide historical findings, focuses on present thinking, and links the research evidence to classroom practice. His highly accessible text covers curriculum policy, teaching materials, teacher orientations and teacher education, student learning, culture studies, and future research. His important alternative views on the teaching of science: • Describe an approach to teaching science (grades 6-12) that animates students’ self-identities, encouraging their future contributions to society as engaged citizens and productive workers. • Address the tension between educationally sound ideas and the politically realities of schools. • Present evidence-based challenges to traditional thinking about school science, illuminating many productive directions for future research.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".