Fairness is based on quality, not just quantity
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 According to decades of research, whether negotiations succeed depends on how much of the stake each person will get. Yet, real-world stakes often consist of resources that vary on quality, not just quantity. While it may appear obvious that people should reject qualitatively inferior offers, just as they reject quantitatively unequal offers, it is less clear why. Across three incentive-compatible studies ( N = 1,303) using the ultimatum game, we evaluate three possible reasons for why people reject qualitatively unequal negotiation offers (that are 50% of the stake): fairness, mere inequality, or badness. Data across the three studies are consistent with the fairness account. Casting doubt on the possibility that people reject qualitatively unequal offers merely because they are ‘bad’, Studies 1 and 2 found that participants were more likely to reject the same coins when these were inferior (e.g., 200 × 5¢ coins) to the negotiation partner’s coins (e.g., 5 × $2 coins) than when both parties received the same undesirable coins (e.g., both received 200 × 5¢ coins). Supporting a fairness explanation, rejection rates of the qualitatively inferior offer were higher when the proposal came from a human (vs. a computer), suggesting that rejection stemmed in part from a desire to punish the negotiation partner for unfair treatment (Study 3). Nevertheless, some participants still rejected the unequal offer from a computer, suggesting that mere inequality matters as well. In sum, the findings highlight that quality, not just quantity, is important for attaining fair negotiation outcomes.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it