Social media and the modern scientist: a research primer for low- and middle-income countries
Bibliographic record
Abstract
Social media has changed the way we communicate. Wherever you are in the world, various forms of social media are being used by individuals to share information and connect without borders. Due to its ubiquity, social media holds great promise in linking clinicians, scientists, investigators, and the public to change the way we conduct scientific discourse. In this paper, we present a step-by-step guide on optimizing your social media strategy with regards to: research/scholarly practice (discourse, collaboration, recruitment), knowledge translation, dissemination, and education. This guide also highlights key readings that provide guidance to those interested in incorporating social media into their scholarly practice.
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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 | 0.021 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.016 | 0.035 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.015 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 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".