Publisher Correction: How decoy options ferment choice biases in real-world consumer decision-making
Bibliographic record
Abstract
Author notes These authors contributed equally: Anya Skatova, A. Ross Otto. Authors and Affiliations Department of Psychology, McGill University, Montreal, Canada Sean Devine & A. Ross Otto N/LAB, Nottingham University Business School, Nottingham, UK James Goulding & John Harvey Digital Footprints Lab & Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Population Health Science, Bristol Medical School, University of Bristol, Bristol, UK Anya Skatova Authors Sean Devine View author publications Search author on: PubMed Google Scholar James Goulding View author publications Search author on: PubMed Google Scholar John Harvey View author publications Search author on: PubMed Google Scholar Anya Skatova View author publications Search author on: PubMed Google Scholar A. Ross Otto View author publications Search author on: PubMed Google Scholar Corresponding author Correspondence to Sean Devine .
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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 | 0.006 | 0.134 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.123 | 0.030 |
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".