Selective Numeracy: Effects of Numeracy, Popular-Science Reportsand Personal Experience on Data-Based Decision Making
Bibliographic record
Abstract
In the current research, we investigated whether numeracy,scientific reports in the popular press, and personal experiencewere associated with people’s data-based decision making.We collected data from English-speaking adult participants(N = 187), residing in the United States and Canada, whowere recruited through Amazon Mechanical Turk andcompleted the online study. Results showed that participantswith higher numeracy were more likely to make the correctdata-based decision. However, participants used theirnumeracy selectively. They seemed to use their numeracyskills to confirm their own desire rather than to objectivelyevaluate the data or confirm reported scientific findings. Nosignificant association was found between personalexperience and data-based decision making. Future researchmay examine decision making across other, general-lifedomains to examine the replicability of the current results.
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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 | 0.005 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".