Spelling Errors and “Shouting” Capitalisation Implicitly Cause Linearly Additive Penalties to Trustworthiness Judgements of Online Health Information: Online Randomised Experiments with Laypersons
Bibliographic record
Abstract
Abstract How people make assessments of trustworthiness remains controversial, as several factors may inflect such assessments and subsequent decisions. This research programme aims to reveal implicit trustworthiness judgements specifically caused by spelling errors and online “shouting” (unnecessary capitalisation of whole words for emphasis). In a series of online experiments, participants were instructed to rate the trustworthiness of the content of nine short text excerpts about multiple sclerosis in the format of posts to an unmoderated health forum. In randomised, counterbalanced designs, some excerpts had no typographic errors, some had spelling errors, some had shouting text, and some had both types of unconventionalities. In linear mixed effects models, unconventionality number or type was coded as a fixed effect. Participants—encoded as random effects to correct statistically for repeated measures from individuals—rated the text samples by marking their judgement of trustworthiness on an unnumbered slider scale. The resulting data showed that multiple unconventionalities caused linearly additive penalties to trustworthiness. Adding more spelling errors caused lower trustworthiness ratings, and spelling errors plus shouting text caused additive trustworthiness penalties. Thus, when participants rated brief scientific information in this context, they implicitly assigned to unconventionalities linearly additive trustworthiness penalties. This contravenes a dichotomous heuristic or local ceiling effect on trustworthiness penalties. It supports an integrative cost–benefit model that includes insight into one's psychological judgements of trustworthiness.
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 | 0.050 | 0.168 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".