Counterclaims and Refutations: An Analysis of Students' Handling of Opposing Arguments
Bibliographic record
Abstract
This paper analyzes student writing to explore how students handle opposing arguments in an argumentative writing activity. Previous research has shown that handling opposition has a positive impact on the readers' perceptions of an argument, while holding a myside bias can weaken both a writer's ethos and the persuasiveness of the argument. The findings here show that many students succumb to myside bias, thus missing the opportunity to handle opposition beyond dismissive or unsubstantiated belief statements. Such statements are seen to weaken ethos and argument. Some students do refute opposing arguments and even qualify their claims with a balanced approach to opposition. This last approach of holding opposition in balance is not discussed in the myside bias literature but suggests an important approach for engaging in fair-minded reasoning. The fact that so many students are missing this essential critical thinking (and writing) skill suggests a need to develop better instruction, including assistance in transferring learning from other contexts where such skills may also have been called upon.
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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 | 0.008 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".