Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Choice lists with random incentives are widely used for preference elicitation. It is commonly assumed that subjects choose the same option in each question as they would have if it were the only question, but recent findings challenge this assumption. We conduct a large sample experiment varying incentives and presentation independently, and examine choices both near and away from certainty. We consistently find more risk taking when a choice between a safe prize and a risky lottery is embedded in a choice list than when it is presented on its own. This difference remains when we inform subjects of the paid choice in advance, implying that isolation fails not because of the random incentives scheme, but simply because the choice appears in a list together with others. We conjecture that subjects are uncertain about their preferences, reduce this uncertainty through considering the choices that confront them, and make cautious decisions in the interim. Other conditions and non-choice data support this interpretation. Our results open up the possibility that preferences inferred from choice lists offer a better indication of informed preferences than preferences inferred from single choices.
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.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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