When Computers Were Human

Grier, David Alan

doi:10.2307/j.ctt4cgc82

Cited by 55 publications

(67 citation statements)

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“…Most social scientists view supply-side explanations as inadequate-among other things, because the gender typing of occupational roles varies strongly across time and space, and over the individual life course (Jacobs 1989;Tolley 2003;Penner 2008;Grier 2005;Charles 2011aCharles , 2017 and because measurable differences between men and women are too small to account for the extreme patterns of segregation observed in many occupations and workplaces. Even when men and women differ on average on some aptitude or personality trait, between-gender differences are typically much smaller than within-gender differences, and the size of observed gender gaps frequently vary by context or disappear when men and women have the same status (Epstein 1988;Ridgeway and Smith-Lovin 1999;Eagly 1995;Hyde 2005;Stoet and Geary 2018).…”

Section: Micro-level Explanationsmentioning

confidence: 99%

Segregation, Stereotypes, and STEM

2018

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Scientific, technical, engineering, and mathematical (STEM) occupations are strongholds of gender segregation in the contemporary United States. While many Americans regard this segregation as natural and inevitable, closer examination reveals a great deal of variability in the gendering of STEM fields across time, space, and demographic groups. This article assesses how different theoretical accounts accord with the available evidence on the gender composition of scientific and technical fields. We find most support for accounts that allow for a dynamic interplay between individual-level traits and the broader sociocultural environments in which they develop. The existing evidence suggests, in particular, that Western cultural stereotypes about the nature of STEM work and STEM workers and about the intrinsic qualities of men and women can be powerful drivers of individual aptitudes, aspirations, and affinities. We offer an illustrative catalog of stereotypes that support women's STEM-avoidance and men's STEM-affinity, and we conclude with some thoughts on policy implications.Keywords: gender; STEM; segregation; stereotypes; culture; work; occupations; science; inequality For more than three decades, American educators, policy makers, activists, and business leaders have engaged in research and policy initiatives to increase the presence of women and other underrepresented groups in scientific, technical, engineering, and mathematical (STEM) occupations and fields of study. These efforts have been motivated by interests in broadening opportunities in lucrative, high-status occupations and in ameliorating acute STEM labor shortages that are believed to threaten national prosperity, private profits and the public welfare.Despite wide-ranging research and policy efforts, STEM occupations remain strongholds of gender segregation in the contemporary United States. Women made up nearly half of the US labor market in 2015, but only 28% of all scientific and technical workers. Within STEM, gender segregation is also very strong, with women comprising 48% of life scientists and 60% of social scientists, but only 28% of physical scientists and 15% of engineers (NSF 2018, Appendices 3-12). 1 While some fields have integrated over time, others have become more segregated. Women's share of US bachelor's degrees in computer science, for instance, declined from 28% to 18% between (NSF 2018.While many Americans understand men's dominance of scientific and technical work as natural and universal, the gender typing of STEM fields varies a great deal across space, time, and socio-demographic groups. Recent comparative studies have shown that scientific degree recipients are disproportionately female in Iran, Saudi Arabia, Romania, and Georgia, for example, and that the gender gap in children's STEM aspirations is larger in more affluent societies (Charles 2011a(Charles , 2017 1 Even within engineering women and men tend to do different work: about 10% of mechanical and electrical engineers are women, compared to 20...

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Section: Micro-level Explanationsmentioning

confidence: 99%

Segregation, Stereotypes, and STEM

2018

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“…This made sense as a simplifying assumption in the 1920s and 1930s, when scholars were developing the modern rules of statistical inference (e.g., Fisher 1925;Grier 2005). In those days, as today, there was uncertainty about what was the best statistical model for a problem.…”

Section: The Model Uncertainty: a Blind Spot In Statistical Analysismentioning

confidence: 99%

Model Uncertainty and the Crisis in Science

Young

2018

Socius: Sociological Research for a Dynamic World

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The "crisis in science" today is rooted in genuine problems of model uncertainty and lack of transparency. Researchers estimate a large number of models in the course of their research but only publish a small number of preferred results. Authors have much influence on the results of an empirical study through their choices about model specification. I advance methods to quantify the influence of the author-or at least demonstrate the scope an author has to choose a preferred result. Multimodel analysis, combined with modern computational power, allows authors to present their preferred estimate alongside a distribution of estimates from many other plausible models. I demonstrate the method using new software and applied empirical examples. When evaluating research results, accounting for model uncertainty and model robustness is at least as important as statistical significance.

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“…The "technological" environment may vary from crafts or schools that practise account keeping or surveying to highly organised industrial or military complexes of research and development. There exist a number of interesting studies on the mathematical practitioners and human computors in such institutionally and socially defined contexts as the observatory (Wepster, 2010 andAubin et al, 2010), the computation bureau (Croarken, 1990 andGrier, 2005), K. Pearson's Biostatistics Lab (J. Barrow-Green in Aubin and Goldstein, 2014) or on German computational work in aerodynamics (Epple et al, 2005).…”

Section: Maarten Bullynckmentioning

confidence: 99%

Histories of algorithms: Past, present and future

Bullynck¹

2016

Historia Mathematica

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International audienceThe book under review, first published some twenty years ago, is the product of a group of French historians of mathematics: Jean-Luc Chabert, Evelyn Barbin, Michel Guillemot, Anne Michel-Pajus, Jacques Borowczyk, Ahmed Djebbar et Jean-Claude Martzloff. An English translation by Chris Weeks was published by Springer in 1999 (Chabert et al., 1999) and a second French edition is now available and will be reviewed here. It was at the time of its first appearance—and is still— the sole book-length study of the history of mathematical algorithms. Therefore this second corrected and extended edition of the French original is most welcome. The original edition of Histoire d'algorithmes was published at a time of developing interest in the history of algorithms within the mathematics community and it was, in part, conceived for use in the mathematics classroom. The historical range of the book is impressive; from Ancient Mesopotamia and Egypt to modern computer developments, this volume endeavours to forge a history of mathematics through highlighting the role of algorithms. The book contains 15 chapters and closes with short biographies of the authors discussed and a general index. Each chapter opens with a general overview that introduces the reader to the chapter's topic. The topic is illustrated and developed through a series of excerpts taken (or translated into French) from the original, historical documents. The text is always followed by a rewriting in terms of elementary modern mathematics and by some analysis and posthistoire of the algorithm. While the main text of the book has been largely kept as it was in the first edition, this more recent posthistoire has been updated for this second edition. The book, one finds, falls naturally into two parts. The first (chapters 1–5), comparative in nature, focuses on the structure of an algorithm at different times in different cultures and traditions, principally, though not exclusively, pre-modern and non-Western. The second part remains largely within the realm of modern Western mathematics (17th–20th centuries) and presents a more or less linear story of how certain kinds of algorithms used in numerical analysis were developed and generalized

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When Computers Were Human

Cited by 55 publications

References 0 publications

Segregation, Stereotypes, and STEM

Segregation, Stereotypes, and STEM

Model Uncertainty and the Crisis in Science

Histories of algorithms: Past, present and future

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