Bibliographic record
Abstract
Ownership is often viewed as demarcating who can use resources and who is restricted from using them. This paper explores another side of ownership—ownership may be attributed to mark individuals as accountable and responsible for causing harm. Across eight experiments, participants (total N = 2517) read vignettes where an agent’s actions led resources to be deposited on others’ land (Experiments 1 to 5) or on unowned land (Experiments 6 to 8). The resources benefitted, harmed, or had no effect on the landowners, or on plants and animals on the land. This manipulation caused an asymmetry between harms and benefits in ownership judgments. Participants more strongly endorsed the agent as owner for harmful resources than beneficial ones, and they also judged it more acceptable for the agent to retrieve harmful resources from others’ land. In contrast, participants more strongly endorsed resources as belonging to landowners or to no one when they were beneficial rather than harmful. We also found that participants endorsed the agent as owning harmful resources even when other means were available for conveying the agent was accountable. Together, our findings show that ownership serves functions besides rewarding individuals with rights over property and besides ensuring individuals are responsible for harm caused by their property—people also attribute ownership to ensure that wrongdoers remain connected and accountable for harm they cause. We discuss implications for theories of ownership, and how our findings relate to other asymmetries between harms and benefits.
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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 | 0.036 |
| 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 | 0.005 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".