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Hierarchy and Testosterone

2019· reference-entry· en· W2970654956 on OpenAlexaff
Shawn N. Geniole, Justin M. Carré

Bibliographic record

Venuenot available
Typereference-entry
Languageen
FieldPsychology
TopicEvolutionary Psychology and Human Behavior
Canadian institutionsNipissing University
Fundersnot available
KeywordsTestosterone (patch)Anticipation (artificial intelligence)HierarchySocial hierarchyRanking (information retrieval)Competition (biology)Social statusPsychologySocial psychologyBiologyEndocrinologyEconomicsEcologyPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Hierarchy, the relative ranking of individuals with respect to status or other social dimensions, is ubiquitous across human social groups. However, relatively little is known about the biological factors that may promote or inhibit mobility in status hierarchies. Prominent theoretic perspectives suggest that concentrations of testosterone in the bloodstream fluctuate dynamically in anticipation of and in response to social challenges, serving to promote behaviors aimed at gaining/maintaining status. This chapter reviews studies that have directly examined the extent to which endogenous, competition-induced surges in testosterone predicted subsequent competitiveness and aggressiveness. Studies suggest that testosterone surges promote competitiveness, but that this effect is complex and depends on several factors. More consistent was evidence that testosterone surges predicted subsequent aggressive behavior. Testosterone–behavior links were also specific to men, suggesting that surges in this hormone may serve different functions, or may promote alternative behavioral strategies for gaining and maintaining status, in women.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.047

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 studies0.0010.001
Scholarly communication0.0020.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0140.003

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 head0.057
GPT teacher head0.345
Teacher spread0.288 · 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
GenreOther

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

Citations7
Published2019
Admission routes1
Has abstractyes

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