Academic explanatory journalism and emerging COVID-19 science: how social media accounts amplify <i>The Conversation</i> ’s preprint coverage
Bibliographic record
Abstract
This article examines the public communication of COVID-19-related ‘preprints’ (unreviewed research studies) in a digital media environment. To understand how preprint research flows from preprint server, to media story, to social media audience, we analysed engagement with ‘second-order citations’ – social media posts linking to media coverage of research – using a sample of 41 media stories published by the research amplifier platform The Conversation (TC) that mentioned preprint research during the early months of the pandemic. We applied content analyses to the Facebook and Twitter accounts sharing these stories and analysed the engagement that the posts received. We found that TC stories mentioning preprints were shared among a diverse collection of Facebook and Twitter accounts, providing a second layer of social media amplification of preprint research. Still, posts by a small proportion of ‘elite’ actors – people with prominent roles in media and communications, politics or academia – tended to generate more engagement.
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 | 0.025 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.023 | 0.016 |
| Open science | 0.001 | 0.013 |
| 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".