The biogeography and evolution of land ownership
Bibliographic record
Abstract
Abstract Aim Land ownership norms are well documented and play a central role in social–ecological systems. Yet only recently has the spatial and temporal distribution of land ownership been examined using biogeographical and evolutionary approaches. We incorporate biogeographical and evolutionary modelling to test associations between land ownership and environmental, subsistence and cultural contact predictors. Location Africa. Taxon Bantu and Bantoid ethnolinguistic groups (73 societies). Methods Based on ethnographies for 73 societies, we coded land ownership norms as none, group, kin or individual. We paired these data with language phylogenies, and measured phylogenetic and geographical signal and modelled alternative evolutionary trajectories using maximum likelihood methods. We tested the influence of environmental, subsistence and cultural predictors on spatial variation in land ownership, using a multi‐model inference approach based on logistic regression. Results Bantu land ownership norms likely evolved on a unilinear trajectory (i.e. societies progress or regress along a series of ownership types), but not one requiring consistent increase in exclusivity (i.e. restrictions towards ownership by smaller groups) as suggested by prior theory. Our biogeographical analyses suggest land ownership is more likely where neighbours also own land and resource productivity is predictable. Reliance on agriculture has relatively small effect sizes and low importance in the model. Main Conclusions We find support for multiple evolutionary pathways. Lack of resolution may be due to localized horizontal transfer of norms consistent with the influence of neighbours we find from biogeographical analyses. We cannot rule out other untested mechanisms. Although long‐standing theories propose links between subsistence practices and land ownership, our results suggest subsistence plays only a modest role. Our results also support resource defensibility theory (i.e. land ownership is more likely where environmental productivity is predictable). Overall, we demonstrate the value of combining analytical approaches from evolution and biogeography to test hypotheses on the spatial and temporal variation of human cultural traits.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".