MétaCan
Menu
Back to cohort
Record W3171887433 · doi:10.31235/osf.io/y5n6z

The Evolution and Ecology of Land Ownership

2021· preprint· en· W3171887433 on OpenAlexaff
Hannah J. Haynie, Geoff Kushnick, Patrick H. Kavanagh, Carol R. Ember, Claire Bowern, Bobbi S. Low, Ty Tuff, Bruno Vilela, Kathryn R. Kirby, Carlos A. Botero, Michael C. Gavin

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicLanguage and cultural evolution
Canadian institutionsUniversity of TorontoMcGill University
Fundersnot available
KeywordsBantu languagesLand tenureSubsistence agricultureEcologyProductivityGeographyResource (disambiguation)AgricultureEconomic geographyEconomicsBiologyEconomic growth

Abstract

fetched live from OpenAlex

Land ownership norms play a central role in social-ecological systems, and have been studied extensively as a component of ethnographies. Yet only recently has the distribution of land ownership norms across cultures been examined from evolutionary and ecological perspectives. Here we incorporate evolutionary and macroecological modelling to test associations between land ownership norms and environmental, subsistence, and cultural contact predictors for societies in the Bantu language family. We find that Bantu land ownership norms likely evolved on a unilinear trajectory, but not necessarily one requiring consistent increase in exclusivity as suggested by prior theory. Our macroecological analyses suggest that Bantu societies are more likely to have some form of ownership when their neighbors also do. We also find an effect of environmental productivity, supporting resource defensibility theory, which posits that land ownership is more likely where productivity is predictable. We find less support for a proposed link between agricultural intensification and land ownership. Overall, we demonstrate the value of combining analytical approaches from evolution and ecology to test diverse hypotheses on land ownership across a range of disciplines.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.505
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.290
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicLanguage and cultural evolutionFrench-language works237,207