Is trust a zero-sum game? What happens when institutional sources get it wrong
Bibliographic record
Abstract
Trust in mainstream institutions is declining while people are increasingly turning to alternative media and conspiracy theories. Previous research has suggested that these trends may be linked, but the dynamics of trust across multiple sources has received little investigation. Is trust a neutral process, where each source is judged independently, is it a zero-sum competition, where a loss for one side is a gain for the other, or does losing trust in one source in foster a more generalized sense of distrust? Across three experimental studies (N = 2,951) we examined how people react when a source makes a serious error, testing four potential models of trust dynamics. We found that regardless of whether the outlet is mainstream, counter-mainstream, or neutral, trust drops for the erring source but does not rise for its competitors. This was the case in the context of both food regulations and COVID-19 precautions. Such a pattern suggest that each source may be judged independently of others. However, in several cases, an error made by one source led to a loss of trust in all sources, suggesting that rather than choosing sides between competing sources, people are also judging the media landscape as a whole to discern if it is feasible to find trustworthy information. However, correlational data did also find that the more people saw a source as politicized, the less they trusted that source and the more they trusted its competitors.
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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 | 0.014 | 0.102 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".