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Record W2133481869 · doi:10.1111/jeb.12253

How to distinguish altruism from spite (and why we should bother)

2013· article· en· W2133481869 on OpenAlexafffund
Daniel Brian Krupp

Bibliographic record

VenueJournal of Evolutionary Biology · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicEvolutionary Game Theory and Cooperation
Canadian institutionsQueen's University
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsAltruism (biology)BiologyAction (physics)Adaptation (eye)Inclusive fitnessNeighbourhood (mathematics)Evolutionary biologyCognitive psychologyPsychology

Abstract

fetched live from OpenAlex

Social behaviour is often described as altruistic, spiteful, selfish or mutually beneficial. These terms are appealing, but it has not always been clear how they are defined and what purpose they serve. Here, I show that the distinctions among them arise from the ways in which fitness is partitioned: none can be drawn when the fitness consequences of an action are wholly aggregated, but they manifest clearly when the consequences are partitioned into primary and secondary (neighbourhood) effects. I argue that the primary interaction is the principal source of adaptive design, because (i) it is this interaction that determines the fit of an adaptation and (ii) it is the actor and primary recipients whom an adaptation foremost affects. The categories of social action are thus instrumental to any account of evolved function.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.424
Threshold uncertainty score0.840

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.029
GPT teacher head0.295
Teacher spread0.266 · 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 designNot applicable
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

Citations22
Published2013
Admission routes2
Has abstractyes

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