Bibliographic record
Abstract
The utility of landscape as an important component of cultural geography continues to evolve. This article demonstrates the utility of the landscape concept in reconciling informal and formal land rights. Land tenure has proved to be one of the most perplexing issues in the developing world. The inability of formal and customary property rights systems to effectively connect in ways that provide for tenure security creates dilemmas not easily overcome. Typical manifestations of this incompatibility involve problems relating to land claims and disputes, which rest upon proving access and ownership rights. Such proof is at the heart of both the capital-poverty-property rights argument and the rights recognition approach, as well as noncommodity, identity-based, and service attachments to lands. This article argues that evidence proving (attesting to) rights to land is an important but overlooked domain of interaction for formal and customary tenure systems and is where opportunity resides for a potential contribution to effective cooperation between tenure regimes. Moreover, this evidence is embedded in the same landscapes that are and have been of interest to cultural geography. This “evidence landscape” is examined in the context of its utility and connection to customary tenure and formal law and how it plays a role in attending to tenurial incompatibility in three cases: Mozambique, East Timor, and the Zuni of the United States.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 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".