Aspects of endowment: A query theory of value construction.
Bibliographic record
Abstract
How do people judge the monetary value of objects? One clue is provided by the typical endowment study (D. Kahneman, J. L. Knetsch, & R. H. Thaler, 1991), in which participants are randomly given either a good, such as a coffee mug, that they may later sell ("sellers") or a choice between the good and amounts of cash ("choosers"). Sellers typically demand at least twice as much as choosers, inconsistent with economic theory. This result is usually explained by an increased weighting of losses, or loss aversion. The authors provide a memory-based account of endowment, suggesting that people construct values by posing a series of queries whose order differs for sellers and choosers. Because of output interference, these queries retrieve different aspects of the object and the medium of exchange, producing different valuations. The authors show that the content and structure of the recalled aspects differ for selling and choosing and that these aspects predict valuations. Merely altering the order in which queries are posed can eliminate the endowment effect, and changing the order of queries can produce endowment-like effects without ownership.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.018 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".