Notes from the periphery: finding more than (non)ownership in property law?
Bibliographic record
Abstract
Property law structures the way we make decisions about how we live together and with the world around us. In doing so, it shapes, but is also shaped by, our relationships with the places we inhabit and encounter. Traditionally, non-owners are defined by their distance and exclusion from the primary legal relationship and their lack of enforceable interests. Yet, land use conflicts continue to arise because people routinely assert relationships with land and resources that they are not formally recognised as owning but with which they are deeply entangled. This chapter touches briefly on three examples: the relations of Indigenous Peoples with fee simple lands within Canada; Māori ownership of freshwater in Aotearoa New Zealand; and claims to public space made by unhoused persons. Though these people–place relations are shaped by their legal definition as non-owner relations, purportedly severed and obscured for legal decision-making, they continue to shape formal legal property relations. As such, they deserve recognition as more than peripheral to property law. This chapter traces the assertion of these ‘more-than-ownership’ relations as part of the necessary work of rebuilding a system of property that sustains us as relational beings embedded in the complex materiality of places.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.055 |
| Scholarly communication | 0.011 | 0.022 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".