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Modeling Group Size and Scalar Stress by Logistic Regression from an Archaeological Perspective

Johnson’s scalar stress theory, describing the mechanics of (and the remedies to) the increase in in-group conflictuality that parallels the increase in groups’ size, provides scholars with a useful theoretical framework for the understanding of different aspects of the material culture of past comm...

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Autor principal: Alberti, Gianmarco
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3953443/
https://www.ncbi.nlm.nih.gov/pubmed/24626241
http://dx.doi.org/10.1371/journal.pone.0091510
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author Alberti, Gianmarco
author_facet Alberti, Gianmarco
author_sort Alberti, Gianmarco
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description Johnson’s scalar stress theory, describing the mechanics of (and the remedies to) the increase in in-group conflictuality that parallels the increase in groups’ size, provides scholars with a useful theoretical framework for the understanding of different aspects of the material culture of past communities (i.e., social organization, communal food consumption, ceramic style, architecture and settlement layout). Due to its relevance in archaeology and anthropology, the article aims at proposing a predictive model of critical level of scalar stress on the basis of community size. Drawing upon Johnson’s theory and on Dunbar’s findings on the cognitive constrains to human group size, a model is built by means of Logistic Regression on the basis of the data on colony fissioning among the Hutterites of North America. On the grounds of the theoretical framework sketched in the first part of the article, the absence or presence of colony fissioning is considered expression of not critical vs. critical level of scalar stress for the sake of the model building. The model, which is also tested against a sample of archaeological and ethnographic cases: a) confirms the existence of a significant relationship between critical scalar stress and group size, setting the issue on firmer statistical grounds; b) allows calculating the intercept and slope of the logistic regression model, which can be used in any time to estimate the probability that a community experienced a critical level of scalar stress; c) allows locating a critical scalar stress threshold at community size 127 (95% CI: 122–132), while the maximum probability of critical scale stress is predicted at size 158 (95% CI: 147–170). The model ultimately provides grounds to assess, for the sake of any further archaeological/anthropological interpretation, the probability that a group reached a hot spot of size development critical for its internal cohesion.
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spelling pubmed-39534432014-03-18 Modeling Group Size and Scalar Stress by Logistic Regression from an Archaeological Perspective Alberti, Gianmarco PLoS One Research Article Johnson’s scalar stress theory, describing the mechanics of (and the remedies to) the increase in in-group conflictuality that parallels the increase in groups’ size, provides scholars with a useful theoretical framework for the understanding of different aspects of the material culture of past communities (i.e., social organization, communal food consumption, ceramic style, architecture and settlement layout). Due to its relevance in archaeology and anthropology, the article aims at proposing a predictive model of critical level of scalar stress on the basis of community size. Drawing upon Johnson’s theory and on Dunbar’s findings on the cognitive constrains to human group size, a model is built by means of Logistic Regression on the basis of the data on colony fissioning among the Hutterites of North America. On the grounds of the theoretical framework sketched in the first part of the article, the absence or presence of colony fissioning is considered expression of not critical vs. critical level of scalar stress for the sake of the model building. The model, which is also tested against a sample of archaeological and ethnographic cases: a) confirms the existence of a significant relationship between critical scalar stress and group size, setting the issue on firmer statistical grounds; b) allows calculating the intercept and slope of the logistic regression model, which can be used in any time to estimate the probability that a community experienced a critical level of scalar stress; c) allows locating a critical scalar stress threshold at community size 127 (95% CI: 122–132), while the maximum probability of critical scale stress is predicted at size 158 (95% CI: 147–170). The model ultimately provides grounds to assess, for the sake of any further archaeological/anthropological interpretation, the probability that a group reached a hot spot of size development critical for its internal cohesion. Public Library of Science 2014-03-13 /pmc/articles/PMC3953443/ /pubmed/24626241 http://dx.doi.org/10.1371/journal.pone.0091510 Text en © 2014 Gianmarco Alberti http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Alberti, Gianmarco
Modeling Group Size and Scalar Stress by Logistic Regression from an Archaeological Perspective
title Modeling Group Size and Scalar Stress by Logistic Regression from an Archaeological Perspective
title_full Modeling Group Size and Scalar Stress by Logistic Regression from an Archaeological Perspective
title_fullStr Modeling Group Size and Scalar Stress by Logistic Regression from an Archaeological Perspective
title_full_unstemmed Modeling Group Size and Scalar Stress by Logistic Regression from an Archaeological Perspective
title_short Modeling Group Size and Scalar Stress by Logistic Regression from an Archaeological Perspective
title_sort modeling group size and scalar stress by logistic regression from an archaeological perspective
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3953443/
https://www.ncbi.nlm.nih.gov/pubmed/24626241
http://dx.doi.org/10.1371/journal.pone.0091510
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