Bibliographic record
Abstract
STEM education has drawn considerable international attention in recent decades. Current practices of STEM Education in China have shown that STEM is understood primarily as a policy-driven and economic problem. This paper challenges the reduced technical frameworks of STEM education and draws on the theoretical framework of the reconceptualization movement in the curriculum field. It aims to shift scientific or social science paradigms with languages of humanity to reapproach STEM education. STEM education is hence reconceptualized as praxis in its lived, ethical and historical dimensions. It would transform STEM education in China from being targeted for economic growth to attending to lived, nuanced and rich STEM experiences, structured by ethics and threaded by history. STEM teachers and teacher educators are advised to understand anew about STEM in their situatedness. STEM could serve as an opportunity to critically reflect on and research each discipline in greater depth with inter-disciplinary lenses. Instead of integrating science, technology, engineering and mathematics seamlessly to form omnipotent toolsets for the 21st-century workforce in China and on the international landscape, a reconceptualization of STEM as praxis could continue to generate more sustainable STEM education.
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 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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".