Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
An essential skill for STEM undergraduates is the ability to understand the world by manipulating, visualizing, and analyzing data to make or evaluate claims. Current online debate, without peer-reviewed literature, explores which of two common R syntax environments (base R or tidyverse) is best for teaching novice R users. In an in-person undergraduate course on evolutionary biology, we implemented two coding curricula: one using base R (n = 49 students) and the other using tidyverse (n = 58 students). We compared these two curricula using several dimensions of student success: interpretation of syntax, creation of appropriate data visualizations and analyses, and an absence of sex bias in performance. A linear model revealed prior experience had the largest estimated effect, followed by syntax environment; sex had the smallest effect. Pedagogical approaches that ensure students have repeated opportunities for practice and that implement techniques to overcome student frustration and anxiety are likely more important than syntax environment when learning coding in biology classes. Furthermore, the small effect of sex combined with the high proportion of females in the biological sciences suggests introducing computer programming in biology may allow females to discover interest and ability that they may not have had if computer programming was the sole propriety of computer science departments.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it