How<i>BJSM</i>embraces the power of social media to disseminate research
Bibliographic record
Abstract
Ways of communication have changed considerably in recent years. The latest innovation is what has become known as ‘social media’. Wikipedia (itself a social platform) defines social media as “the means of interactions among people in which they create, share and exchange information and ideas in virtual communities and networks.”1 You probably use some different social websites, for example, Twitter, Facebook, LinkedIn, ResearchGate, YouTube, Skype, Flickr, Wordpress, Reddit, Mendeley, etc. These are the more popular among a much larger mix of social platforms. The foremost strength of social media is the constant availability of interaction with your peers through mobile devices. Today, smart phones pack the computing power of a low-end laptop in the palm of your hand. Thereby, you can communicate and share messages, pictures, videos and music, with your social network wherever you are and whenever you want. You do not need to be an active user of social media to grasp its potential. You can be a so-called listener or lurker. “In most online communities, 90% of users are lurkers who never contribute, 9% of users contribute a little and 1% of users account for almost all the action.”2 The practical use of sifting through discussions and other people's messages is that you can follow topics of interest (eg, concussion), colleagues (eg, @RoaldBahr, @ProfJillCook) or journals, thereby gaining a very quick view of the …
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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 | 0.028 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.041 | 0.023 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.013 | 0.009 |
| Insufficient payload (model declined to judge) | 0.044 | 0.038 |
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".