Hero: Let's Do It Your Way
Bibliographic record
Abstract
Examples Pairs of individuals often face situations in which they have a common interest in coordinating their behaviors, but different preferences for the particular combination of behaviors that will be chosen. Among the varieties of this situation is the one considered here, referred to as “Hero” (for reasons to be explained below). In this particular case, the two persons have a strong mutual desire to coordinate their actions, but also a mild conflict of interest about which particular action to pursue among those necessary for such coordination. For example, in close relationships partners often place great value on doing certain things together, such as going to movies or jogging together. However, they may differ in their preferences for which movie to see or where to jog. A husband may prefer to see a comedy, and the wife a crime movie. Despite this difference, their primary consideration is their strong, mutual interest in engaging in a shared activity and enjoying each other's company. The issue they face then is not whether to go to a movie together or separately, but rather how to determine whose preferred movie they will attend jointly. In these circumstances, the opportunity exists for one partner to “play the hero” by volunteering to go to the movie the other prefers. These types of situations are likely to occur frequently in the everyday adjustments and coordinating decisions that friends or partners in close relationships must make.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.009 |
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".