The Development of Territory-Based Inferences of Ownership
Bibliographic record
Abstract
Legal systems often rule that people own objects in their territory. We propose that an early-developing ability to make territory-based inferences of ownership helps children address informational demands presented by ownership. Across 6 experiments (N = 504), we show that these inferences develop between ages 3 and 5 and stem from two aspects of the psychology of ownership. First, we find that a basic ability to infer that people own objects in their territory is already present at age 3 (Experiment 1). Children even make these inferences when the territory owner unintentionally acquired the objects and was unaware of them (Experiments 2 and 3). Second, we find that between ages 3 and 5, children come to consider past events in these judgments. They move from solely considering the current location of an object in territory-based inferences, to also considering and possibly inferring where it originated (Experiments 4 to 6). Together, these findings suggest that territory-based inferences of ownership are unlikely to be constructions of the law. Instead, they may reflect basic intuitions about ownership that operate from early in development.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".