Mothers and Daughters in Nineteenth-Century America: The Biosocial Construction of Femininity

The feminine script of early nineteenth century centered on women’s role as patient, long-suffering mothers. By mid-century, however, their daughters faced a world very different in social and economic options and in the physical experiences surrounding their bodies. In this groundbreaking study, Nancy Theriot turns to social and medical history, developmental psychology, and feminist theory to explain the fundamental shift in women’s concepts of femininity and gender identity during the course of the century—from an ideal suffering womanhood to emphasis on female control of physical self. Theriot\u27s first chapter proposes a methodological shift that expands the interdisciplinary horizons of women\u27s history. She argues that social psychological theories, recent work in literary criticism, and new philosophical work on subjectivities can provide helpful lenses for viewing mothers and children and for connecting socioeconomic change and ideological change. She recommends that women\u27s historians take bolder steps to historicize the female body by making use of the theoretical insights of feminist philosophers, literary critics, and anthropologists. Within this methodological perspective, Theriot reads medical texts and woman- authored advice literature and autobiographies. She relates the early nineteenth-century notion of true womanhood to the socioeconomic and somatic realities of middle-class women\u27s lives, particularly to their experience of the new male obstetrics. The generation of women born early in the century, in a close mother/daughter world, taught their daughters the feminine script by word and action. Their daughters, however, the first generation to benefit greatly from professional medicine, had less reason than their mothers to associate womanhood with pain and suffering. The new concept of femininity they created incorporated maternal teaching but altered it to make meaningful their own very different experience. This provocative study applies interdisciplinary methodology to new and long-standing questions in women\u27s history and invites women\u27s historians to explore alternative explanatory frameworks. Nancy M. Theriot is associate professor of history and chair of the women\u27s studies program at the University of Louisville A significant contribution to our understanding of white middle-class women in the 1800s. —American Studies Theriot\u27s work is readable, well-researched, and thoroughly interdisciplinary. —JASAT This book is outstanding for its discussion of how women interpreted their experience through the media of medical texts, autobiographies, and woman-to-woman advice writing. —The Reader\u27s Reviewhttps://uknowledge.uky.edu/upk_womens_studies/1006/thumbnail.jp

Data from: Crowds replicate performance of scientific experts scoring phylogenetic matrices of phenotypes

Scientists building the Tree of Life face an overwhelming challenge to categorize phenotypes (e.g., anatomy, physiology) from millions of living and fossil species. This biodiversity challenge far outstrips the capacities of trained scientific experts. Here we explore whether crowdsourcing can be used to collect matrix data on a large scale with the participation of the non-expert students, or “citizen scientists.” Crowdsourcing, or data collection by non-experts, frequently via the internet, has enabled scientists to tackle some large-scale data collection challenges too massive for individuals or scientific teams alone. The quality of work by non-expert crowds is, however, often questioned and little data has been collected on how such crowds perform on complex tasks such as phylogenetic character coding. We studied a crowd of over 600 non-experts, and found that they could use images to identify anatomical similarity (hypotheses of homology) with an average accuracy of 82% compared to scores provided by experts in the field. This performance pattern held across the Tree of Life, from protists to vertebrates. We introduce a procedure that predicts the difficulty of each character and that can be used to assign harder characters to experts and easier characters to a non-expert crowd for scoring. We test this procedure in a controlled experiment comparing crowd scores to those of experts and show that crowds can produce matrices with over 90% of cells scored correctly while reducing the number of cells to be scored by experts by 50%. Preparation time, including image collection and processing, for a crowdsourcing experiment is significant, and does not currently save time of scientific experts overall. However, if innovations in automation or robotics can reduce such effort, then large-scale implementation of our method could greatly increase the collective scientific knowledge of species phenotypes for phylogenetic tree building. For the field of crowdsourcing, we provide a rare study with ground truth, or an experimental control that many studies lack, and contribute new methods on how to coordinate the work of experts and non-experts. We show that there are important instances in which crowd consensus is not a good proxy for correctness

Next-generation phenomics for the Tree of Life

The phenotype represents a critical interface between the genome and the envi-ronment in which organismslive and evolve. Phenotypic characters also are a rich source of biodiversity data for tree-building, and theyenable scientists to reconstruct the evolu-tionary history of organisms, including most fossil taxa, for whichgenetic data are unavail-able. Therefore, phenotypic data are necessary for building a comprehensive Tree ofLife. In contrast to recent advances in molecular sequencing, which has become faster and cheaper throughrecent technological advances, phenotypic data collection remains often prohibi-tively slow and expensive. Thenext-generation phenomics project is a collaborative, multidisciplinary effort to leverage advances in imageanalysis, crowdsourcing, and natural language processing to develop and implement novel approaches fordiscovering and scor-ing the phenome, the collection of phentotypic characters for a species. This research represents a new approach to data collection that has the potential to transform phylogenetics research and toenable rapid advances in constructing the Tree of Life. Our goal is to as-semble large phenomic datasets builtusing new methods and to provide the public and sci-entific community with tools for phenomic data assemblythat will enable rapid and auto-mated study of phenotypes across the Tree of Life

Appendix 20 Instructions to Crowd

Online Appendix 20. Instruction sheets for the study participants (undergraduate students at Ohio State University)

Appendix 14 Marine-Shrimp-user-results

Online Appendix 14. Marine shrimp user scores

Appendix 4 Anemones-user-results

Online Appendix 4. Sea anemones user scores. For each crowd member, we report the number of scores they provided and the number that were correct. The “Estimate” column is the probability that this crowd member voted correctly, and the “ci.lower” column gives the 95% lower confidence bound on this probability. These scores are for all characters (evaluation and test)

Appendix 9 Diatoms-character-taxon-results

Online Appendix 9. Diatom character score

Appendix 3 Anemones-character-taxon-results

Online Appendix 3. Sea anemones character scores. For each character and taxon in the anemones matrix, we show the probability (“Estimate”) that a crowd member’s score would agree with the majority vote of the crowd. We also show the lower confidence interval on this probability (ci.lower), which is the crowd confidence score. Finally, we indicate whether the majority vote was correct, and compute an ROC curve for the crowd’s scores. The Threshold Plot worksheet provides a visualization of this information