The Idea of Property: A Comparative Review of Recent Empirical Research Methods
Bibliographic record
Abstract
While theory offers important insights into property's normative content, it sometimes fails to tell us about what people understand property to mean and how they interact with those things said to be owned by them. This has significant implications for some of the challenges facing humanity, including climate change, unequal distributions of wealth and resources, biodiversity loss, and innovation. In response, a growing body of literature is emerging that looks at property through a different lens; rather than theorizing property in an abstract way or attempting to craft a normative account of and justification for the institution, this new scholarship focuses on everyday people's views and experiences-what some call the psychology of property and what we call the idea of property. This article presents a comparative review of empirical research methods that the authors have recently used to study the idea (or psychology) of property and provides evidence drawn from the United States, Canada, and Australia: (i) Stenseth's work on behavioral economics and property law; (ii) Metcalf's empirical research drawing on social psychology and behavioral economics; and (iii) the small-scale, qualitative study conducted by Babie, Burdon, and da Rimini. All three studies suggest that individuals hold an idea of property that exists independently from the formal law found in the jurisdiction studied. Moreover, while individuals do appear willing to self-regulate with reference to the environment or for the public good, for the most part people's idea of property is one that allows for promoting individual desires. Whether this is innate, culturally determined, or both is beyond this article's scope, but we conclude that this is an important area for future research and investigation.
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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 | 0.062 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.035 | 0.040 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".