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Record W2002725840 · doi:10.1111/jeb.12648

The evolution of respect for property

2015· review· en· W2002725840 on OpenAlexaff
Thomas N. Sherratt, Mike Mesterton‐Gibbons

Bibliographic record

VenueJournal of Evolutionary Biology · 2015
Typereview
Languageen
FieldSocial Sciences
TopicProperty Rights and Legal Doctrine
Canadian institutionsCarleton University
FundersSimons Foundation
KeywordsBiologyProperty (philosophy)Evolutionary biologyEpistemology

Abstract

fetched live from OpenAlex

Although possession is 'nine-tenths of the law', respect for ownership is widespread in the animal kingdom even without third-party enforcement. Thus, the first individuals to find objects are frequently left unchallenged by potential competitors and tend to win contests when disputes arise. Game theory has shown that respect for ownership ('Bourgeois' behaviour) can arise as an arbitrary convention to avoid costly disputes. However, the same theory predicts that a paradoxical respect for lack of ownership ('anti-Bourgeois' behaviour) can evolve under the same conditions and in some cases is the only stable outcome. Despite these predictions, anti-Bourgeois behaviour is rare in nature, whereas respect for ownership is frequently not absolute. Here, we review extensions of the classic models involving repeated interactions, confusion over roles, strategic coordination of behaviour ('secret handshakes'), owner-intruder asymmetries and continuous control of fighting investment. Confusion over roles and owner-intruder asymmetries in fighting ability may explain why respect for ownership is often partial. Moreover, although most model extensions facilitate the evolution of Bourgeois-like behaviour, secret handshakes and continuous control of fighting investment render the alternative anti-Bourgeois convention unstable. We develop these insights to highlight several key areas for future investigation.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.004
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.123
GPT teacher head0.414
Teacher spread0.291 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations79
Published2015
Admission routes1
Has abstractyes

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