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The Evolutionary History of the Human Face
This is a repository copy of The evolutionary history of the human face. White Rose Research Online URL for this paper: https://eprints.whiterose.ac.uk/145560/ Version: Accepted Version Article: Lacruz, Rodrigo S, Stringer, Chris B, Kimbel, William H et al. (5 more authors) (2019) The evolutionary history of the human face. Nature Ecology and Evolution. pp. 726-736. ISSN 2397-334X https://doi.org/10.1038/s41559-019-0865-7 Reuse Items deposited in White Rose Research Online are protected by copyright, with all rights reserved unless indicated otherwise. They may be downloaded and/or printed for private study, or other acts as permitted by national copyright laws. The publisher or other rights holders may allow further reproduction and re-use of the full text version. This is indicated by the licence information on the White Rose Research Online record for the item. Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing [email protected] including the URL of the record and the reason for the withdrawal request. [email protected] https://eprints.whiterose.ac.uk/ THE EVOLUTIONARY HISTORY OF THE HUMAN FACE Rodrigo S. Lacruz1*, Chris B. Stringer2, William H. Kimbel3, Bernard Wood4, Katerina Harvati5, Paul O’Higgins6, Timothy G. Bromage7, Juan-Luis Arsuaga8 1* Department of Basic Science and Craniofacial Biology, New York University College of Dentistry; and NYCEP, New York, USA. 2 Department of Earth Sciences, Natural History Museum, London, UK 3 Institute of Human Origins and School of Human Evolution and Social Change, Arizona State University, Tempe, AZ. -
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VIRGINIA LAW REVIEW ASSOCIATION VIRGINIA LAW REVIEW ONLINE VOLUME 103 DECEMBER 2017 94–102 ESSAY ACT-SAMPLING BIAS AND THE SHROUDING OF REPEAT OFFENDING Ian Ayres, Michael Chwe and Jessica Ladd∗ A college president needs to know how many sexual assaults on her campus are caused by repeat offenders. If repeat offenders are responsible for most sexual assaults, the institutional response should focus on identifying and removing perpetrators. But how many offenders are repeat offenders? Ideally, we would find all offenders and then see how many are repeat offenders. Short of this, we could observe a random sample of offenders. But in real life, we observe a sample of sexual assaults, not a sample of offenders. In this paper, we explain how drawing naive conclusions from “act sampling”—sampling people’s actions instead of sampling the population—can make us grossly underestimate the proportion of repeat actors. We call this “act-sampling bias.” This bias is especially severe when the sample of known acts is small, as in sexual assault, which is among the least likely of crimes to be reported.1 In general, act sampling can bias our conclusions in unexpected ways. For example, if we use act sampling to collect a set of people, and then these people truthfully report whether they are repeat actors, we can overestimate the proportion of repeat actors. * Ayres is the William K. Townsend Professor at Yale Law School; Chwe is Professor of Political Science at the University of California, Los Angeles; and Ladd is the Founder and CEO of Callisto. 1 David Cantor et al., Report on the AAU Campus Climate Survey on Sexual Assault and Sexual Misconduct, Westat, at iv (2015) [hereinafter AAU Survey], (noting that a relatively small percentage of the most serious offenses are reported) https://perma.cc/5BX7-GQPU; Nat’ Inst. -
Upper Pleistocene Human Remains from Vindija Cave, Croatia, Yugoslavia
AMERICAN JOURNAL OF PHYSICAL ANTHROPOLOGY 54~499-545(1981) Upper Pleistocene Human Remains From Vindija Cave, Croatia, Yugoslavia MILFORD H. WOLPOFF, FRED H. SMITH, MIRKO MALEZ, JAKOV RADOVCIC, AND DARKO RUKAVINA Department of Anthropology, University of Michigan, Ann Arbor, Michigan 48109 (MH.W.1,Department of Anthropology, University of Tennessee, Kmxuille, Tennessee 37916 (FHS.),and Institute for Paleontology and Quaternary Geology, Yugoslav Academy of Sciences and Arts, 41000 Zagreb, Yugoslavia (M.M., J.R., D.R.) KEY WORDS Vindija, Neandertal, South Central Europe, Modern Homo sapiens origin, Evolution ABSTRACT Human remains excavated from Vindija cave include a large although fragmentary sample of late Mousterian-associated specimens and a few additional individuals from the overlying early Upper Paleolithic levels. The Mousterian-associated sample is similar to European Neandertals from other regions. Compared with earlier Neandertals from south central Europe, this sam- ple evinces evolutionary trends in the direction of Upper Paleolithic Europeans. Compared with the western European Neandertals, the same trends can be demon- strated, although the magnitude of difference is less, and there is a potential for confusing temporal with regional sources of variation. The early Upper Paleo- lithic-associated sample cannot be distinguished from the Mousterian-associated hominids. We believe that this site provides support for Hrdlicka’s “Neandertal phase” of human evolution, as it was originally applied in Europe. The Pannonian Basin and surrounding val- the earliest chronometrically dated Upper leys of south central Europe have yielded a Paleolithic-associated hominid in Europe large and significant series of Upper Pleisto- (Smith, 1976a). cene fossil hominids (e.g. Jelinek, 1969) as well This report presents a detailed comparative as extensive evidence of their cultural behavior description of a sample of fossil hominids re- (e.g. -
Human Dimensions of Wildlife the Fallacy of Online Surveys: No Data Are Better Than Bad Data
Human Dimensions of Wildlife, 15:55–64, 2010 Copyright © Taylor & Francis Group, LLC ISSN: 1087-1209 print / 1533-158X online DOI: 10.1080/10871200903244250 UHDW1087-12091533-158XHuman Dimensions of WildlifeWildlife, Vol.The 15, No. 1, November 2009: pp. 0–0 Fallacy of Online Surveys: No Data Are Better Than Bad Data TheM. D. Fallacy Duda ofand Online J. L. Nobile Surveys MARK DAMIAN DUDA AND JOANNE L. NOBILE Responsive Management, Harrisonburg, Virginia, USA Internet or online surveys have become attractive to fish and wildlife agencies as an economical way to measure constituents’ opinions and attitudes on a variety of issues. Online surveys, however, can have several drawbacks that affect the scientific validity of the data. We describe four basic problems that online surveys currently present to researchers and then discuss three research projects conducted in collaboration with state fish and wildlife agencies that illustrate these drawbacks. Each research project involved an online survey and/or a corresponding random telephone survey or non- response bias analysis. Systematic elimination of portions of the sample population in the online survey is demonstrated in each research project (i.e., the definition of bias). One research project involved a closed population, which enabled a direct comparison of telephone and online results with the total population. Keywords Internet surveys, sample validity, SLOP surveys, public opinion, non- response bias Introduction Fish and wildlife and outdoor recreation professionals use public opinion and attitude sur- veys to facilitate understanding their constituents. When the surveys are scientifically valid and unbiased, this information is useful for organizational planning. Survey research, however, costs money. -
Levy, Marc A., “Sampling Bias Does Not Exaggerate Climate-Conflict Claims,” Nature Climate Change 8,6 (442)
Levy, Marc A., “Sampling bias does not exaggerate climate-conflict claims,” Nature Climate Change 8,6 (442) https://doi.org/10.1038/s41558-018-0170-5 Final pre-publication text To the Editor – In a recent Letter, Adams et al1 argue that claims regarding climate-conflict links are overstated because of sampling bias. However, this conclusion rests on logical fallacies and conceptual misunderstanding. There is some sampling bias, but it does not have the claimed effect. Suggesting that a more representative literature would generate a lower estimate of climate- conflict links is a case of begging the question. It only make sense if one already accepts the conclusion that the links are overstated. Otherwise it is possible that more representative cases might lead to stronger estimates. In fact, correcting sampling bias generally does tend to increase effect estimates2,3. The authors’ claim that the literature’s disproportionate focus on Africa undermines sustainable development and climate adaptation rests on the same fallacy. What if the climate-conflict links are as strong as people think? It is far from obvious that acting as if they were not would somehow enhance development and adaptation. The authors offer no reasoning to support such a claim, and the notion that security and development are best addressed in concert is consistent with much political theory and practice4,5,6. Conceptually, the authors apply a curious kind of “piling on” perspective in which each new paper somehow ratchets up the consensus view of a country’s climate-conflict links, without regard to methods or findings. Consider the papers cited as examples of how selecting cases on the conflict variable exaggerates the link. -
10. Sample Bias, Bias of Selection and Double-Blind
SEC 4 Page 1 of 8 10. SAMPLE BIAS, BIAS OF SELECTION AND DOUBLE-BLIND 10.1 SAMPLE BIAS: In statistics, sampling bias is a bias in which a sample is collected in such a way that some members of the intended population are less likely to be included than others. It results in abiased sample, a non-random sample[1] of a population (or non-human factors) in which all individuals, or instances, were not equally likely to have been selected.[2] If this is not accounted for, results can be erroneously attributed to the phenomenon under study rather than to the method of sampling. Medical sources sometimes refer to sampling bias as ascertainment bias.[3][4] Ascertainment bias has basically the same definition,[5][6] but is still sometimes classified as a separate type of bias Types of sampling bias Selection from a specific real area. For example, a survey of high school students to measure teenage use of illegal drugs will be a biased sample because it does not include home-schooled students or dropouts. A sample is also biased if certain members are underrepresented or overrepresented relative to others in the population. For example, a "man on the street" interview which selects people who walk by a certain location is going to have an overrepresentation of healthy individuals who are more likely to be out of the home than individuals with a chronic illness. This may be an extreme form of biased sampling, because certain members of the population are totally excluded from the sample (that is, they have zero probability of being selected). -
Cognitive Biases in Software Engineering: a Systematic Mapping Study
Cognitive Biases in Software Engineering: A Systematic Mapping Study Rahul Mohanani, Iflaah Salman, Burak Turhan, Member, IEEE, Pilar Rodriguez and Paul Ralph Abstract—One source of software project challenges and failures is the systematic errors introduced by human cognitive biases. Although extensively explored in cognitive psychology, investigations concerning cognitive biases have only recently gained popularity in software engineering research. This paper therefore systematically maps, aggregates and synthesizes the literature on cognitive biases in software engineering to generate a comprehensive body of knowledge, understand state of the art research and provide guidelines for future research and practise. Focusing on bias antecedents, effects and mitigation techniques, we identified 65 articles (published between 1990 and 2016), which investigate 37 cognitive biases. Despite strong and increasing interest, the results reveal a scarcity of research on mitigation techniques and poor theoretical foundations in understanding and interpreting cognitive biases. Although bias-related research has generated many new insights in the software engineering community, specific bias mitigation techniques are still needed for software professionals to overcome the deleterious effects of cognitive biases on their work. Index Terms—Antecedents of cognitive bias. cognitive bias. debiasing, effects of cognitive bias. software engineering, systematic mapping. 1 INTRODUCTION OGNITIVE biases are systematic deviations from op- knowledge. No analogous review of SE research exists. The timal reasoning [1], [2]. In other words, they are re- purpose of this study is therefore as follows: curring errors in thinking, or patterns of bad judgment Purpose: to review, summarize and synthesize the current observable in different people and contexts. A well-known state of software engineering research involving cognitive example is confirmation bias—the tendency to pay more at- biases. -
Correcting Sampling Bias in Non-Market Valuation with Kernel Mean Matching
CORRECTING SAMPLING BIAS IN NON-MARKET VALUATION WITH KERNEL MEAN MATCHING Rui Zhang Department of Agricultural and Applied Economics University of Georgia [email protected] Selected Paper prepared for presentation at the 2017 Agricultural & Applied Economics Association Annual Meeting, Chicago, Illinois, July 30 - August 1 Copyright 2017 by Rui Zhang. All rights reserved. Readers may make verbatim copies of this document for non-commercial purposes by any means, provided that this copyright notice appears on all such copies. Abstract Non-response is common in surveys used in non-market valuation studies and can bias the parameter estimates and mean willingness to pay (WTP) estimates. One approach to correct this bias is to reweight the sample so that the distribution of the characteristic variables of the sample can match that of the population. We use a machine learning algorism Kernel Mean Matching (KMM) to produce resampling weights in a non-parametric manner. We test KMM’s performance through Monte Carlo simulations under multiple scenarios and show that KMM can effectively correct mean WTP estimates, especially when the sample size is small and sampling process depends on covariates. We also confirm KMM’s robustness to skewed bid design and model misspecification. Key Words: contingent valuation, Kernel Mean Matching, non-response, bias correction, willingness to pay 2 1. Introduction Nonrandom sampling can bias the contingent valuation estimates in two ways. Firstly, when the sample selection process depends on the covariate, the WTP estimates are biased due to the divergence between the covariate distributions of the sample and the population, even the parameter estimates are consistent; this is usually called non-response bias. -
New Fossils from Jebel Irhoud, Morocco and the Pan-African Origin of Homo Sapiens Jean-Jacques Hublin1,2, Abdelouahed Ben-Ncer3, Shara E
LETTER doi:10.1038/nature22336 New fossils from Jebel Irhoud, Morocco and the pan-African origin of Homo sapiens Jean-Jacques Hublin1,2, Abdelouahed Ben-Ncer3, Shara E. Bailey4, Sarah E. Freidline1, Simon Neubauer1, Matthew M. Skinner5, Inga Bergmann1, Adeline Le Cabec1, Stefano Benazzi6, Katerina Harvati7 & Philipp Gunz1 Fossil evidence points to an African origin of Homo sapiens from a group called either H. heidelbergensis or H. rhodesiensis. However, a the exact place and time of emergence of H. sapiens remain obscure because the fossil record is scarce and the chronological age of many key specimens remains uncertain. In particular, it is unclear whether the present day ‘modern’ morphology rapidly emerged approximately 200 thousand years ago (ka) among earlier representatives of H. sapiens1 or evolved gradually over the last 400 thousand years2. Here we report newly discovered human fossils from Jebel Irhoud, Morocco, and interpret the affinities of the hominins from this site with other archaic and recent human groups. We identified a mosaic of features including facial, mandibular and dental morphology that aligns the Jebel Irhoud material with early or recent anatomically modern humans and more primitive neurocranial and endocranial morphology. In combination with an age of 315 ± 34 thousand years (as determined by thermoluminescence dating)3, this evidence makes Jebel Irhoud the oldest and richest African Middle Stone Age hominin site that documents early stages of the H. sapiens clade in which key features of modern morphology were established. Furthermore, it shows that the evolutionary processes behind the emergence of H. sapiens involved the whole African continent. In 1960, mining operations in the Jebel Irhoud massif 55 km south- east of Safi, Morocco exposed a Palaeolithic site in the Pleistocene filling of a karstic network. -
Quantifying Aristotle's Fallacies
mathematics Article Quantifying Aristotle’s Fallacies Evangelos Athanassopoulos 1,* and Michael Gr. Voskoglou 2 1 Independent Researcher, Giannakopoulou 39, 27300 Gastouni, Greece 2 Department of Applied Mathematics, Graduate Technological Educational Institute of Western Greece, 22334 Patras, Greece; [email protected] or [email protected] * Correspondence: [email protected] Received: 20 July 2020; Accepted: 18 August 2020; Published: 21 August 2020 Abstract: Fallacies are logically false statements which are often considered to be true. In the “Sophistical Refutations”, the last of his six works on Logic, Aristotle identified the first thirteen of today’s many known fallacies and divided them into linguistic and non-linguistic ones. A serious problem with fallacies is that, due to their bivalent texture, they can under certain conditions disorient the nonexpert. It is, therefore, very useful to quantify each fallacy by determining the “gravity” of its consequences. This is the target of the present work, where for historical and practical reasons—the fallacies are too many to deal with all of them—our attention is restricted to Aristotle’s fallacies only. However, the tools (Probability, Statistics and Fuzzy Logic) and the methods that we use for quantifying Aristotle’s fallacies could be also used for quantifying any other fallacy, which gives the required generality to our study. Keywords: logical fallacies; Aristotle’s fallacies; probability; statistical literacy; critical thinking; fuzzy logic (FL) 1. Introduction Fallacies are logically false statements that are often considered to be true. The first fallacies appeared in the literature simultaneously with the generation of Aristotle’s bivalent Logic. In the “Sophistical Refutations” (Sophistici Elenchi), the last chapter of the collection of his six works on logic—which was named by his followers, the Peripatetics, as “Organon” (Instrument)—the great ancient Greek philosopher identified thirteen fallacies and divided them in two categories, the linguistic and non-linguistic fallacies [1]. -
Human Origin Sites and the World Heritage Convention in Eurasia
World Heritage papers41 HEADWORLD HERITAGES 4 Human Origin Sites and the World Heritage Convention in Eurasia VOLUME I In support of UNESCO’s 70th Anniversary Celebrations United Nations [ Cultural Organization Human Origin Sites and the World Heritage Convention in Eurasia Nuria Sanz, Editor General Coordinator of HEADS Programme on Human Evolution HEADS 4 VOLUME I Published in 2015 by the United Nations Educational, Scientific and Cultural Organization, 7, place de Fontenoy, 75352 Paris 07 SP, France and the UNESCO Office in Mexico, Presidente Masaryk 526, Polanco, Miguel Hidalgo, 11550 Ciudad de Mexico, D.F., Mexico. © UNESCO 2015 ISBN 978-92-3-100107-9 This publication is available in Open Access under the Attribution-ShareAlike 3.0 IGO (CC-BY-SA 3.0 IGO) license (http://creativecommons.org/licenses/by-sa/3.0/igo/). By using the content of this publication, the users accept to be bound by the terms of use of the UNESCO Open Access Repository (http://www.unesco.org/open-access/terms-use-ccbysa-en). The designations employed and the presentation of material throughout this publication do not imply the expression of any opinion whatsoever on the part of UNESCO concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. The ideas and opinions expressed in this publication are those of the authors; they are not necessarily those of UNESCO and do not commit the Organization. Cover Photos: Top: Hohle Fels excavation. © Harry Vetter bottom (from left to right): Petroglyphs from Sikachi-Alyan rock art site. -
New Techniques for Time-Activity Studies of Avian Flocks in View-Restricted Habitats
j. Field Ornithol., 60(3):388-396 NEW TECHNIQUES FOR TIME-ACTIVITY STUDIES OF AVIAN FLOCKS IN VIEW-RESTRICTED HABITATS MICHAEL P. LOSITO,• RALPH E. MIRARCHI, AND GUY A. BALDASSARRE 1 Departmentof Zoologyand WildlifeScience AlabamaAgricultural Experiment Station Auburn University,Alabama 36849-5414 USA Abstract.--Focal-animal sampling was comparedto a newly developedtechnique, focal- switchsampling, to evaluatetime budgetsof Mourning Doves(Zenaida macroura) in habitats where the field of view was restricted.Focal-switch sampling included a formula employed to weighthabitat use and testfor restrictionsof habitatstructure on behavior,and a standard waiting period to decidewhen to end sampling or continue pursuit of lost flocks. Focal- animal samplingbiased estimates of activebehavior downward, whereas estimates of inactive behaviorwere similar for both methods.Focal-switch sampling increased research efficiency by 12%. The standardwaiting period saved24% of samplesfrom prematuretermination and reducedobserver bias by maintaining equal sampling effort per flock. Weighting of habitat use also reducedsampling bias. Focal-switch sampling is recommendedfor use in conjunctionwith focal-animalsampling when samplingin view-restrictedhabitats. NUEVAS TP•CNICASPARA EL ESTUDIO DE ACTIVIDADES DE CONGREGACIONES DE AVES EN HABITATS CON VISIBILIDAD RESTRINGIDA Resumen.--Lat6cnica de observaci6ndirecta de animales(focal-animal) es comparada con un nuevom6todo en dondese evaluan los presupuestosde actividadesen Zenaidamacroura en habitatsen dondeel campode visi6nestaba restringido. La nuevat6cnica (cambio-focal) incluyeuna f6rmulapara "pesar"la utilizaci6nde habitaty medirlas restriccionesque imponenla estructuradel habitaten la conducta.Incluye ademfis,un periodode espera estandarizadoque permite al observadordecidir cuando terminar susobservaciones o conti- nuar las mismas.E1 muestreo de un soloanimal (focal-animal)tiene un sesgopara estimar la conductaactira, mientras que los estimados para conducta inactiva rueton similares para ambosm6todos.