Have You Seen This? Why Political Pundits Share Scholarly Research on Social Media
Bibliographic record
Abstract
Background A healthy public sphere requires a flow of reliable, trustworthy, and accurate information. Scholarly research is one such source but, to be most effective, it must reach the public. One possible dissemination route for that material is political pundits. Analysis We extracted the tweets of thirty-two Canadian pundits with links to scholarly research and studied the main motivations for sharing a link to a scholarly article. Conclusion and implications We found that most pundits we studied tweeted at least one link to a scholarly article and that the motivations for sharing varied. However, our sample shared links to scholarly journal articles infrequently. Résumé Contexte Pour bien fonctionner, une sphère publique requiert un flux d’informations qui soient fiables, dignes de confiance et précises. La recherche savante est une source de telles informations, mais pour être efficace elle doit rejoindre le public. Une façon de disséminer la recherche consiste à recourir à des commentateurs politiques. Analyse Nous avons passé en revue les gazouillis de 32 commentateurs canadiens ayant des liens avec la recherche savante et nous avons étudié leurs motivations principales pour partager un lien vers un article savant. Conclusion et implications Nous avons découvert que la plupart des commentateurs de notre échantillon ont inclus au moins un lien vers un article savant dans leurs gazouillis et que leurs motivations pour le faire étaient diverses. Cependant, ces commentateurs ne partageaient pas souvent des liens vers des articles paraissant dansdes revues savantes.
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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 | 0.007 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".