Bibliographic record
Abstract
Since the beginning of the new millennium, educational research and policy making have increasingly involved integration of science, technology, engineering and mathematics (i.e. STEM). Integration of the four disciplines is argued to provide students with contextualized learning experiences that resemble real-life work in STEM fields, along with solutions to interdisciplinary problems that human face. In the U.S., the STEM movement has been boosted by global economic-based competition and associated fears, in terms of STEM graduates, when compared with other nations. However, many critiques question the nature and goals of this competition, as well as, the possibilities to improve STEM talents through the current conceptualizations and practices of STEM education. Through Lyotard’s (1984) conceptions of knowledge in the postmodern society, this paper analyzes some aspects of the STEM educational movement. It explores the construction of STEM discourse within competitive frames that place prime value on high performativity. There seem to be two characteristics of current STEM education that support performativity; these are an increased focus on technological and engineering designs, and a tendency for interdisciplinary education/curriculum integration. At the same time, the eagerness for performativity and competition seems to drag STEM education into selectiveness, thereby jeopardizing its possible benefits. Recommendations for educators are finally discussed.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.054 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".