Attending to adversarial science communication: a commentary on Lewenstein and Baram-Tsabari’s vision of science communication education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it