Science Communication with TED Talks: A Conceptual Replication of Sugimoto and Thelwall
Bibliographic record
Abstract
This study uses the latest data and data concepts to replicate and validate Sugimoto and Thelwall’s 2013 analysis of the impact of media platforms on scientific and scholarly communication and the dissemination of scientific research. The current study analyzes the use of 1491 TED Talk videos—videos frequently used to communicate scientific research and scholarship—on two platforms: the TED website and YouTube. The results suggest that the impact of the videos on knowledge dissemination was stronger across four metrics than in Sugimoto and Thelwall’s study and that art and design videos continue to receive less attention than other types of videos (e.g., science and technology). Also, academic speakers received more comments than other types of presenters. To improve science communication with videos, we offer suggestions drawn from this research to help presenters better communicate science through the use of online videos. Résumé Cette étude recourt à des données récentes et à des concepts sur les données récents pour reproduire et valider une analyse effectuée par Sugimoto et Thelwall en 2013 sur la manière dont les plateformes médiatiques influencent la communication savante et la diffusion de la recherche scientifique. Notre étude analyse l’utilisation de 1 491 vidéos de TED Talk—fréquemment utilisées pour communiquer la recherche et le savoir scientifiques—sur deux plateformes : le site web de TED et YouTube. Les résultats suggèrent que l’impact des vidéos sur la diffusion des connaissances était plus fort sur quatre paramètres clés que dans l’étude de Sugimoto et Thelwall et que les vidéos sur l’art et le design continuent de recevoir moins d’attention que d’autres types de vidéos (par exemple, celles sur la science et la technologie). De même, les présentateurs académiques ont reçu plus de commentaires que d’autres types de présentateurs. Afin d’améliorer la communication scientifique par vidéos, nous faisons, en guise de conclusion, des suggestions basées sur notre recherche pour aider les présentateurs à mieux parler de science au moyen de vidéos en ligne.
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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 | 0.115 | 0.304 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.015 | 0.030 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".