Charting a course for effective scientific communication: Balancing accuracy and promotion around the Virgin Galactic crash
Bibliographic record
Abstract
Coverage of the inflight explosion of Virgin Galactic's SpaceShipTwo on October 31st, 2014, exposes a gap between Virgin's polished public face and the technical realities of its aim to provide accessible space travel. In the days following the crash, Branson used his various media arms to communicate concern, support, and his faith in the importance of his company's space mission. Meanwhile, the National Transportation Safety Board (NTSB) began their ongoing investigation into the incident, releasing four video briefings in the week following the crash. These two media events — Branson's online handling of the fallout from the incident, and the NTSB's Acting Chairman Chris Hart's briefings — provide an interesting case study in the way that scientific material is disseminated by and for expert and non-expert audiences, and how this interacts with expectations around the marketing and promotion of scientific discovery. This paper will analyze the contrasting coverage of Virgin Galactic's SpaceShipTwo crash from the NTSB, Virgin's media arms and popular journalism, to consider how we balance scientific accuracy with attempts to capture and promote public interest in the sciences.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.012 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".