Bibliographic record
Abstract
The legal principle of accession suggests that people sometimes extend ownership of a prominent item to related objects, resources, and benefits. For example, people might assume that whoever owns a large land mass is also likely to possess surrounding islands. In three experiments on 4-7-year-olds (N=526) and adults (N=498), we find that prominence affects inductive inferences about both ownership and liking. In Experiment 1, children were more likely to infer that a character owned various individual blocks when the character was initially described as owning a pile of blocks (prominent) than a single block (regular). Experiment 2 replicated this pattern in both children and adults, and found it extends across three stimuli sets, but not a fourth. Experiment 3 then revealed that prominence also affects children’s and adults’ inductive inferences about preferences. Together, the findings suggest that prominence affects inductive inferences of both ownership and preferences, and the findings likewise suggest that parts of property law could reflect aspects of psychology present from early in the lifespan.
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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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".