Role of Scientific Theory in Simulation Education Research
Bibliographic record
Abstract
STATEMENT: Scientific theories are consistent explanations about how the world works. They have been shown to be plausible not only from a large amount of independent confirmatory evidence but also because rigorous attempts at falsification have failed. Other desirable features include parsimony, scalability, explanatory, and predictive power. Scientific theories differ from models and laws in the amount of evidence available and/or the degree to which they explain nature. Learning curve theory is a scientific theory with direct applicability to simulation education researchers. In this article, the authors use the example of learning curve theory to illustrate the key features of scientific theories and how they provide a meaningful foundation for simulation-based education research programs.
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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 | 0.166 | 0.274 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.005 | 0.058 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".