PERFORMING SCIENCE: SCIENCE COMMUNICATION AS PERFORMATIVE EVENT
Bibliographic record
Abstract
A course at the University of Toronto encourages engineering students to analyze how science isconveyed in the popular media through a variety of contexts. An analysis of the language and rhetoric of these communicative acts provides on entry point into how science is framed, while the discipline of performance studies, which identifies and analyzes the mechanisms with which we present our messages and ourselves, provides another useful tool through which to understand the motivations and associated strategies behind scientific communication. This teaching practice paper presents three case studies of scientific press conferences used in the course: NASA’s 2010 astrobiology event, the Higgs Boson announcement in 2012, and Virgin Galactic’s 2014 SpaceShipTwo crash. These three case studies illustrate how the act of communicating science within public spaces should be navigated with an awareness of the intended message and the way that this message is conveyed and perceived. Each case study includes a summary of observations on the event (generated and shared through class discussions), and prompts that will enable theeffective instruction of these and other case studies.
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.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.025 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".