Writing Toward a Scientific Identity: Shifting From Prescriptive to Reflective Writing in Undergraduate Biology
Bibliographic record
Abstract
Analytical writing enhances retention of science learning and is integral to student-centered classrooms. Despite this, scientific writing in undergraduate programs is often presented as a series of sentence-level conventions of grammar, syntax, and citation formats, reinforcing students’ perceptions of its highly prescriptive nature. We designed our research to transform students’ perceptions of scientific writing in an upper level class in biology through a semester-long, inquiry-based writing project. Results of surveys before the activity suggested that students perceived writing as prescriptive rather than reflective. For example, many believed that their results should not contradict existing scientific knowledge or that their discussions should not point out the shortcomings of research. One semester of student-centered inquiry through writing increased students’ confidence to challenge existing knowledge and improved their understanding of their role as communicators. Despite this, our results suggest that activities that model exploring and questioning existing knowledge in science promise to give students even greater confidence in their scientific writing.
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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 | 0.017 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".