Feeling the heat: undergraduate science students’ emotional management during classroom debates
Bibliographic record
Abstract
Addressing a need to prepare the next generation of scientists to effectively engage in adversarial science communication, the present study examines a group of undergraduate science students from a Canadian university who, after receiving expert instruction, participated in classroom debates about science controversies recently politicized in the Canadian social media (e.g. the flat Earth, genetically-modified foods, and human overpopulation). Our research questions were: (1) What emotions were experienced and how were these managed by students while participating in classroom debates? (2) How did students’ emotional management influence their debate performance? A video-based micro-ethnography revealed that more than half of the students (16/28) experienced feelings of stress and nervousness when engaging debaters with opposing/disagreeing views. Although some were able to manage these emotions, others were unable to feel relaxed, which negatively influenced their debate performance. These latter students’ initial confidence and preparation were undermined by their felt anxiety, leading to rhetorically weak and error-filled performances that went against their expectations. Highlighting the complexity of pedagogically promoting student development of communicative competence in adversarial social contexts, our findings reveal a need for science communication instructors to find ways to effectively prepare science students to manage their own emotions.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".