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Testosterone and Human Aggression

2017· other· en· W2914367552 on OpenAlexaff
Justin M. Carré, Erika L. Ruddick, Benjamin J. P. Moreau, Brian M. Bird

Bibliographic record

VenueThe Wiley Handbook of Violence and Aggression · 2017
Typeother
Languageen
FieldPsychology
TopicEvolutionary Psychology and Human Behavior
Canadian institutionsSimon Fraser UniversityNipissing University
Fundersnot available
KeywordsAggressionTestosterone (patch)Context (archaeology)PsychologyHormoneCompetition (biology)Developmental psychologyBiologyEcologyEndocrinology

Abstract

fetched live from OpenAlex

Abstract One of the most widely studied biological correlates of aggressive behavior is the steroid hormone testosterone. Although traditional wisdom might suggest that individuals with more testosterone are more likely to be aggressive, research over the past several decades has identified important contextual, individual difference, and methodological variables that are key moderators of any such effect. In this chapter, we review literature examining how aggression is linked with baseline levels of testosterone, how testosterone fluctuates rapidly within the context of human competitive behavior, and how such competition‐induced hormonal fluctuations serve to potentiate ongoing and/or future aggressive behavior. The neuroendocrine mechanisms underlying such complex social behavior are discussed from research conducted within humans as well as nonhuman species, providing comparative clues as to the adaptive nature of such intricate systems.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.011
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0110.002

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 head0.028
GPT teacher head0.344
Teacher spread0.316 · 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 designTheoretical or conceptual
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

Citations15
Published2017
Admission routes1
Has abstractyes

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