Additional file 1 of A comparison study of human examples vs. non-human examples in an evolution lesson leads to differential impacts on student learning experiences in an introductory biology course
Bibliographic record
Abstract
Additional file 1: Figure S1. Histograms of pre- and post- discomfort scores for each year of the study. Figure S2. Pearson correlations between the pre-class and post-class scores and human evolution acceptance measures using listwise-deletion data. Figure S3. Observed TTCI Pre- scores (blue) and imputed values for the 100 datasets (red). Figure S4. Observed TTCI Post- scores (blue) and imputed values for the 100 datasets (red). Figure S5. Observed Relevance (course)scores (blue) and imputed values for the 100 datasets (red). Figure S6. Observed Relevance (lesson) scores (blue) and imputed values for the 100 datasets (red). Figure S7. Observed Engagement (course) scores (blue) and imputed values for the 100 datasets (red). Figure S8. Observed engagement (lesson) scores (blue) and imputed values for the 100 datasets (red). Figure S9. Observed Discomfort (course) scores (blue) and imputed values for the 100 datasets (red). Figure S10. Observed Discomfort (lesson) scores (blue) and imputed values for the 100 datasets (red). Figure S11. Distribution of imputed TTCI scores for 100 datasets (red) compared to observed scores (blue). Figure S12. Distribution of imputed Relevance scores for 100 datasets (red) compared to observed scores (blue). RelPre refers to perceived relevance of the course content and RelPost refers to perceived relevance of the lesson content. Figure S13. Distribution of imputed Engagement scores for 100 datasets (red) compared to observed scores (blue). EngPre refers to engagement with the course content and EngPost refers to Engagement with the lesson content. Figure S14. Distribution of imputed Discomfort scores for 100 datasets (red) compared to observed scores (blue). DiscPre refers to discomfort with the course content and DiscPost refers to discomfort with the lesson content. Table S1. Frequency of missing data patterns. Table S2. Main effects model results for student post-test TTCI scores. Table S3. Main effects model results for student reported engagement and content relevance during the one-day activity. Table S4. Full model results for student reported discomfort experienced during the one-day activity.
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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 | 0.003 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.802 | 0.070 |
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".