The role of language in students’ justifications of scientific phenomena
Bibliographic record
Abstract
Making decisions and constructing arguments with scientific evidence and reasoning are essential skills for all members of society, especially in a world facing complex socioscientific issues (climate change, global pandemics, etc.). Argumentation is a complex linguistic practice, but little is known about how students from diverse language backgrounds engage in argumentation. The goal of this study was to identify how students’ English language proficiency/history was associated with the reasoning demonstrated in their written arguments. We found that students with lower English proficiency and less English history produced fewer causal responses compared to students with higher English language proficiency and history. Follow-up interviews with fifteen participants revealed that students’ comfort communicating in English on assessments depended on a combination of general and academic language experiences. Findings suggest a need to identify what barriers students from diverse language backgrounds encounter during argumentation to ensure students from all language backgrounds have equitable opportunities to demonstrate their abilities.
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How this classification was reachedexpand
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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".