Bibliographic record
Abstract
In five independent studies evidence is presented consistent with the altruist-detection assumption of Frank's (1988) commitment model. What is unique about these findings is that they were attained using different methodological paradigms from different disciplines. Study One and Two's methodology was borrowed from cognitive psychology and suggests that humans have decision rules designed to detect an altruistic reputation. Study Three uses a standard social psychological methodology, i.e., the zero-acquaintance video-presentation paradigm in an inter-cultural context. Specifically, in zero-acquaintance video encounters with students speaking Dutch, Canadian students detected altruism level. Study Four (A & B) departs from an experimental approach by exploring the spontaneously occurring nonverbal behaviours of altruists and non-altruists in two cultures, i.e., Canadian and Dutch. Findings corroborate the nonverbal behaviour pathway to altruist detection, which claims that hard-to-fake facial expressions vary with altruism level. Finally, Study Five uses an experimental economics approach to test whether smile symmetry and reputation influence resource allocations. As predicted, subjects delivered more resources to cartoon icons with an altruistic reputation and cartoon icons with symmetrical smiles. These results are consistent with Frank's (1988) reputation and nonverbal pathways to altruist detection. If cooperative individuals are reliably identified, they may form alliances, which could have allowed for the selection of genes predisposing altruism in ancestral environments.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".