Humanistic school science: Research, policy, politics and classrooms
Bibliographic record
Abstract
Abstract This article establishes a rational, feasible, and necessary conclusion to reform high school science content into an equitable experience for its wide diversity of students' self‐identities. Research indicates that 85% of graduates would not normally have enrolled in any science course unless required. Their values are more aligned with their everyday world and/or the world of the humanities, to varying degrees. The 15% had already fulfilled their science prerequisite for postsecondary science‐related programs, to varying degrees. The article's conclusion rests mainly on historical and economic evidence, respectively: (1) The Sputnik crisis that instilled public fear and anxiety about the perceived technological gap between the United States and Soviet Union. This led to reforming high school science and implementing National Aeronautics and Space Administration. (2) The on‐growing climate‐change crisis for which the smart international money is increasingly investing in sustainable businesses and industries, which catalyze a shift in public values from the current “profit society” to a “sustainable society.” The article's rationale connects the two historical events. Over the past 30 years, the nature of normal science has evolved into post‐normal science. Today the public square also includes: (a) an international assessment project that receives a negative validity audit in this article; (b) a vocal small minority within the 85%, proud of their antiscience self‐identities and their leaders' hostile behavior (a problem to ameliorate by a reformed sustainable science education); and (c) instances of small‐scale, suitable reform examples developed over the last 70 years, often referred to as humanistic school science.
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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.022 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.001 |
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".