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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".