People and Places: The ‘River’ and ‘Desert Island’ Models of Territory
Bibliographic record
Abstract
This paper offers a comparative examination of two models of territory: the ‘River’ (Nine, Ochoa Espejo) and the ‘Desert Island’/self-determination (Moore, Stilz) theories. This paper argues first that the central contrast between the two is less stark than it first appears. However, there are several dimensions in which the accounts are contrasting, specifically, the importance of group or collective self-determination, and, relatedly, boundary drawing, which, on the ‘Desert Island’ model involves self-determining entities having standing to enter into agreements when sharing or joint jurisdiction is needed, whereas the ‘River’ model suggests that unified jurisdiction is necessary. The paper offers a qualified defence of the ‘Desert Island’ model. It credits the Desert Island model with a more intuitive and coherent approach to group self-determination and therefore to defining ‘places’. It also argues that unified jurisdiction is not necessary to realize the goods of cooperation and shared access to places and resources.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".