Leveraging Social Media in the Stem Cell Sector: Exploring Twitter's Potential as a Vehicle for Public Information Campaigns
Why this work is in the frame
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Bibliographic record
Abstract
AIM: Our aim in this project was to explore Twitter's potential as a vehicle for an online public information campaign (PIC) focused on providing evidence-based information about stem cell therapies and the market for unproven stem cell-based interventions. METHODS: We designed an online, Twitter-based PIC using classic design principles and identified a set of target intermediaries (organizations with online influence) using a network governance approach. We tracked the PIC's dissemination over a 2-month period, and evaluated it using metrics from the #SMMStandards Conclave. RESULTS: Participation was limited but the PIC achieved some reach and engagement. CONCLUSION: Social media based online PICs appear to have potential but also face challenges. Future research is required to better understand how to most effectively maximize their strengths.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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