Psychology of Human Kin Recognition: Heuristic Cues, Erroneous Inferences, and Their Implications
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
Humans possess explicit, rule-based, and culturally determined systems for identifying kin, but kinship inferences are also influenced implicitly by cue-based mechanisms found commonly across the animal kingdom. These mechanisms are fallible. An evolutionarily informed signal-detection analysis suggests that (a) cue-based kin recognition may sometimes be biased in favor of false-positive errors, resulting in implicit kinship inferences even in the presence of nonkin, and (b) the tendency toward this inferential error may vary predictably in response to specific developmental and contextual circumstances. This analysis has important implications for a wide variety of psychological phenomena (especially in the realms of person perception, interpersonal attraction, and prosocial behavior) and leads to the deduction of many novel hypotheses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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