Facilitating Scientific Communication Between Strangers: A Preregistered Lost E-Mail Experiment
Bibliographic record
Abstract
Communication scholars are increasingly concerned about biases that shape people's interactions with science. Past study has focused on echo chambers (cultivating social networks that reinforce existing worldviews). People's facilitation of scientific discourse between strangers also may be shaped by their attitudes. To study the latter, we employed a recent adaptation of Milgram's lost letter technique called the lost e-mail technique (LET). We conducted a preregistered field study using a large undergraduate university sample (N = 1,508) to examine how the LET might elucidate people's treatment of scientific information. We distributed four ostensibly misaddressed scientific messages and monitored the likelihood of these e-mails being facilitated by participants. Participants' beliefs about self-esteem's importance, assessed months earlier, were associated with increased facilitation of scientific claims congruent with (vs. incongruent with) these beliefs. Thus, people shape the spread of online information in a manner matching their beliefs, even for people outside their social networks.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".