Scientists on Twitter: Preaching to the choir or singing from the rooftops?
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
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it