Bibliographic record
Abstract
Social media is any networked ICT tool or platform that derives its content and principal value from user engagement and permits those users to interact with that content as part of a larger movement in communications organized under Web 2.0 (7,8). The ability to comment, share, contribute to and remix existing content is what distinguishes social media from other forms such as television, print, radio and early websites. Social media shifts health communication messaging from one-to-many to include one-toone and many-to-many simultaneously, while offering novel means to reach people wherever they are located in real time. Unlike previous generations of the Web, social media doesn’t require its users to have an understanding of how their tools work or programming languages to generate content and share it. Although social media has been around since 2004, the widespread availability of mobile Internet-enabled devices using Apple’s iOS (iPhone), or Android or Blackberry systems, has put it in reach of people across the globe. Social media users are akin to artists, creating, reworking and sharing content instead of passively ‘consuming’ it. Social media may be new, but its manifestation was presaged through ideas introduced in the 1960s by Marshall McLuhan and members of the Toronto School of Communications group of scholars (9). School member and anthropologist Edmund Snow Carpenter (10) noted how the following ‘rules’ of communication used in traditional journalism ran contrary to what new media offered:
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 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".