How Well Do Experts Understand End-Users’ Perceptions of Manipulative Patterns?
Why this work is in the frame
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Bibliographic record
Abstract
How well do experts understand end-users’ perceptions of manipulative patterns? We conducted online surveys with end-users and with experts assessing perceptions of manipulative patterns. Participants saw images of interfaces and evaluated each through a series of semantic scales (e.g., deceitful to honest). After being shown a definition of manipulative patterns, they then decided whether each interface exemplified a manipulative pattern. End-users correctly identified images as manipulative approximately half of the time, and though experts were more often correct, the differences were not statistically significant. However, end-users’ descriptions of the images were significantly more positive than experts assumed, resulting in experts over-estimating end-users’ ability to recognize when they were being manipulated by an interface.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it