THE NATURE OF SCIENTIFIC EVIDENCE AND ITS IMPLICATIONS FOR TEACHING SCIENCE
Bibliographic record
Abstract
Scientific evidence-based reasoning has been recognized as a form of reasoning that characterizes scientific thinking. This study questioned what scientific evidence means in the various types of scientific activities; that is, this study explored the nature of scientific evidence (NOSE). To do this, previous studies were examined to understand how scientific evidence was analyzed, evaluated, and utilized during the scientific activities of scientists or students in scientific or everyday situations. Through this process, seven statements were identified to describe the NOSE. This study explains these seven NOSE statements, constructs a process of scientific evidence-based reasoning as a structured form by reflecting these seven statements comprehensively, and discusses the practical implications for teaching science in schools. Finally, the limitations of this study are discussed, and possible directions for future studies are suggested. It is believed that the list of NOSE characteristics can provide a starting point for further elucidation and discussion of scientific evidence and helping students’ science learning in more authentic ways. Keywords: evidence evaluation, evidence-based reasoning, evidence-based response, idea-based response, scientific evidence
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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 | 0.133 | 0.333 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| 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".