Performing ‘the scientist,’ credibly and authentically: understanding how scientists manage their self-presentation on social media
Bibliographic record
Abstract
This study examines how scientists construct and manage their self-presentation on social media amid diverse audience expectations by analyzing data collected via semi-structured interviews (N = 24) with US, Canadian, and European scientists with over 10,000 followers on various social media platforms. A reflexive thematic analysis revealed that these scientists' self-presentation practices were broadly informed by three key, often overlapping, goals: to (a) humanize and challenge stereotypes of scientists, (b) build trust and credibility through authenticity, and (c) push back against exclusionary narratives within STEM. Each of these goals required negotiating with platform expectations, professional norms, and audience pressures. In foregrounding individual scientists as key actors in the science communication ecosystem, this study contributes to a more nuanced understanding of how scientists’ self-presentation can become a dynamic site where both individual identity and public perceptions of scientific credibility, trust, and authenticity can be performed and co-constructed in real time. These performances are shaped by their own impression management goals, audience management strategies, as well as broader institutional, sociocultural, and platform norms and expectations.
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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 | 0.024 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".