The varying relationship between helping and individual quality
Bibliographic record
Abstract
Individuals of different quality often differ in their helping behavior, but sometimes it is the high-quality individuals who help most (e.g., human meat sharing, vigilance) and other times it is the low-quality individuals (e.g., reproductive queues, primate grooming). We argue that these differences depend on individual differences in the performance costs of actually helping, the opportunity costs from forsaking alternative activities, and the fitness benefits for engaging the help. If helping is more difficult for some individuals to do (quality-dependent help), it will usually be done by high-quality individuals, whereas help that all individuals could do equally well (quality-independent help) will be done by whoever pays lower opportunity costs. Our model makes novel predictions about many kinds of helping, allows us to categorize different types of helping by their relationship with individual quality, and is general enough to apply to many situations. Furthermore, it can be generalized to any other type of (nonhelping) behavior where there are individual differences in benefits, performance costs, or opportunity costs.
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".