Cardiovascular Research and social media: connecting with researchers, advancing science
Bibliographic record
Abstract
COVID-19 has changed the way people work and live. People are working from home and using different methods of communicating, collaborating, and conducting research. At this time, social media has never played a more important role and has created a new means of working. Cardiovascular Research strives to be a beacon of scientific excellence and publish meaningful basic and translational science, supporting the European Society of Cardiology’s (ESC) mission ‘to reduce the burden of cardiovascular disease’. Key to the Journal’s aim in driving scientific discovery and clinical delivery, Cardiovascular Research disseminates ground-breaking research,1 utilizing social media to engage young and experienced, basic science and clinical researchers alike, as well as the general public to share the latest discoveries. Whilst there are many social media platforms, Twitter attracts a large and growing interest from both cardiologists and the wider community.2 Cardiovascular Research’s Twitter account (@CVR_TomaszGuzik) has experienced steadily increasing growth and active engagement in the last 18 months drawn by the Journal’s innovative content that is relevant and useful, whilst being accessible and providing readers with an opportunity to interact with the Journal and each other, as well as further supporting the next generation of cardiovascular researchers.
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 | 0.029 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.026 | 0.036 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".