scholarly journals What Might Books Be Teaching Young Children About Gender?

2021 ◽  
pp. 095679762110246
Author(s):  
Molly Lewis ◽  
Matt Cooper Borkenhagen ◽  
Ellen Converse ◽  
Gary Lupyan ◽  
Mark S. Seidenberg

We investigated how gender is represented in children’s books using a novel 200,000-word corpus comprising 247 popular, contemporary books for young children. Using adult human judgments and word co-occurrence data, we quantified gender biases of words in individual books and in the whole corpus. We found that children’s books contain many words that adults judge as gendered. Semantic analyses based on co-occurrence data yielded word clusters related to gender stereotypes (e.g., feminine: emotions; masculine: tools). Co-occurrence data also indicated that many books instantiate gender stereotypes identified in other research (e.g., girls are better at reading, and boys are better at math). Finally, we used large-scale data to estimate the gender distribution of the audience for individual books, and we found that children are more often exposed to stereotypes for their own gender. Together, the data suggest that children’s books may be an early source of gender associations and stereotypes.

2020 ◽  
Author(s):  
Molly Lewis ◽  
M. Cooper Borkenhagen ◽  
Ellen Converse ◽  
Gary Lupyan ◽  
Mark S. Seidenberg

We investigate how gender is represented in children’s books using a novel 200,000 word corpus comprising 247 popular, contemporary books for young children (0-5 years). Using human judgments and word co-occurrence data, we quantified gender biases of words in individual books and in the whole corpus. We find that children’s books contain many words that adults judge as gendered. Semantic analyses based on co-occurrence data yielded word clusters related to gender stereotypes (e.g., feminine: emotions; masculine: tools). Co-occurrence data also indicate that many books instantiate gender stereotypes identified in other research (e.g., girls are better at reading and boys at math). Finally, we used large-scale data to estimate the gender distribution of the audience for individual books, and find that children tend to be exposed to gender stereotypes for their own gender. Together the data suggest that children’s books may be an early source of gender associations and stereotypes.


2009 ◽  
Vol 28 (11) ◽  
pp. 2737-2740
Author(s):  
Xiao ZHANG ◽  
Shan WANG ◽  
Na LIAN

2016 ◽  
Author(s):  
John W. Williams ◽  
◽  
Simon Goring ◽  
Eric Grimm ◽  
Jason McLachlan

2008 ◽  
Vol 9 (10) ◽  
pp. 1373-1381 ◽  
Author(s):  
Ding-yin Xia ◽  
Fei Wu ◽  
Xu-qing Zhang ◽  
Yue-ting Zhuang

2021 ◽  
Vol 77 (2) ◽  
pp. 98-108
Author(s):  
R. M. Churchill ◽  
C. S. Chang ◽  
J. Choi ◽  
J. Wong ◽  
S. Klasky ◽  
...  

Author(s):  
Krzysztof Jurczuk ◽  
Marcin Czajkowski ◽  
Marek Kretowski

AbstractThis paper concerns the evolutionary induction of decision trees (DT) for large-scale data. Such a global approach is one of the alternatives to the top-down inducers. It searches for the tree structure and tests simultaneously and thus gives improvements in the prediction and size of resulting classifiers in many situations. However, it is the population-based and iterative approach that can be too computationally demanding to apply for big data mining directly. The paper demonstrates that this barrier can be overcome by smart distributed/parallel processing. Moreover, we ask the question whether the global approach can truly compete with the greedy systems for large-scale data. For this purpose, we propose a novel multi-GPU approach. It incorporates the knowledge of global DT induction and evolutionary algorithm parallelization together with efficient utilization of memory and computing GPU’s resources. The searches for the tree structure and tests are performed simultaneously on a CPU, while the fitness calculations are delegated to GPUs. Data-parallel decomposition strategy and CUDA framework are applied. Experimental validation is performed on both artificial and real-life datasets. In both cases, the obtained acceleration is very satisfactory. The solution is able to process even billions of instances in a few hours on a single workstation equipped with 4 GPUs. The impact of data characteristics (size and dimension) on convergence and speedup of the evolutionary search is also shown. When the number of GPUs grows, nearly linear scalability is observed what suggests that data size boundaries for evolutionary DT mining are fading.


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