Attending to adversarial science communication: a commentary on Lewenstein and Baram-Tsabari’s vision of science communication education
Bibliographic record
Abstract
Unlike K-12 science teachers who can turn to national documents such as the Next Generation Science Standards for guidance on what knowledge and skills are central to their disciplines, university educators who set out to teach science communication are faced with the challenge of having to develop/implement a curriculum without the benefit of a well-established disciplinary core. In the present commentary, we discuss how the framework proposed by Lewenstein and Baram-Tsabari’s (Citation2022) begins to address this issue by taking a first step toward the articulation of a blueprint of science communication education. The commentary is organized as follows. First, Lewenstein and Baram-Tsabari’s (Citation2022) article is considered in light of prior work by other science communication scholars. Attention then shifts to what our own research has revealed as an important absence in Lewenstein and Baram-Tsabari’s (Citation2022) framework, namely the lack of attention given to training in adversarial science communication (e.g. addressing pseudoscience online, public debates). We then end by suggesting ways to attend to this issue, while emphasizing the need for continued field-wide (re)formulation of a common educational vision in/for the teaching and learning of science communication.
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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 | 0.018 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.021 | 0.028 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.063 | 0.068 |
| 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".