Scientists on Twitter: Preaching to the choir or singing from the rooftops?
Bibliographic record
Abstract
There have been strong calls for scientists to share their discoveries with society. Some scientists have heeded these calls through social media platforms such as Twitter. Here, we ask whether Twitter allows scientists to promote their findings primarily to other scientists (“inreach”), or whether it can help them reach broader, non-scientific audiences (“outreach”). We analyzed the Twitter followers of more than 100 faculty members in ecology and evolutionary biology and found that their followers are, on average, predominantly (∼55%) other scientists. However, beyond a threshold of ∼1000 followers, the range of follower types became more diverse and included research and educational organizations, media, members of the public with no stated association with science, and a small number of decision-makers. This varied audience was, in turn, followed by more people, resulting in an exponential increase in the social media reach of tweeting academic scientists. Tweeting, therefore, has the potential to disseminate scientific information widely after initial efforts to gain followers. These results should encourage scientists to invest in building a social media presence for scientific outreach.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".