Buyers Versus Sellers: How They Differ in Their Responses to Framed Outcomes
Bibliographic record
Abstract
Consumers’ reactions to a difference in price can depend on how it is framed. If buyers interpret paying $60 rather than $65 as getting a $5 discount, then they are likely to consider paying $60 to be a gain and paying $65 to be a nongain. Alternatively, if they interpret having to pay $65 rather than $60 as incurring a $5 penalty, then they may consider paying $60 to be a nonloss and paying $65 to be a loss. Similarly, sellers can also experience gains, nongains, nonlosses, and losses. This article suggests that buyers are prevention focused and consequently place a greater emphasis on loss‐related frames, whereas sellers are promotion focused and place a greater emphasis on gain‐related frames. Therefore, for equivalent positive outcomes, buyers feel better about nonlosses, but sellers feel better about gains. For equivalent negative outcomes, buyers feel worse about losses, but sellers feel worse about nongains. These effects, however, disappear when there is little motivation to process information about the monetary transaction.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".