Sucky stuff in re women at work: link roundup

“The current study has implications for group decisions in general, and jury deliberations in particular, by suggesting that expressing anger might lead men to gain influence, but women to lose influence over others (even when making identical arguments).”

“The only thing we have to fear is fear itself.”
Woman in a Meeting: “I have to say — I’m sorry — I have to say this. I don’t think we should be as scared of non-fear things as maybe we are? If that makes sense? Sorry, I feel like I’m rambling.”

“[Jennifer Lawrence] says that she blamed herself for not negotiating harder but also explores why it was she didn’t push that hard. Admitting that it’s “difficult to speak about my experience as a working woman because I can safely say my problems aren’t exactly relatable” — she was negotiating over millions of dollars — she speaks to a dynamic that is all-too-relatable to the rest of us non-millionaire women too.”

“Faculty participants rated the male applicant as significantly more competent and hireable than the (identical) female applicant.”

Results across experiments showed that men evaluate the gender-bias research less favorably than women, and, of concern, this gender difference was especially prominent among STEM faculty (experiment 2). These results suggest a relative reluctance among men, especially faculty men within STEM, to accept evidence of gender biases in STEM.”

Abrasive alone was used 17 times to describe 13 different women, but the word never appeared in men’s reviews. In fact, this type of character critique that was absent from men’s reviews showed up in 71 of the 94 critical reviews received by women.”

race and ethnicity are really just constructs

I thoroughly enjoy Lisa Wade’s Sociological Images blog. In a recent post, she discusses how race definitions have changed over time, and how we ended up labeling white/black/Asian as races and Latino/Hispanic as ethnicity. Over the history of the Census, the race question has at times included “free” status, mixed race, and specific countries of origin. And Wade writes,

“Part of the reason we have the “Hispanic” ethnicity question is because Mexican Americans fought for it.  They thought it would be advantageous to be categorized as “white” and, so, they fought for an ethnicity category instead of a racial one.”

Fascinating, eh?

creating survey data for once

I have been using survey data in my work for about 2 decades, and I finally get the chance to submit information for a survey! I must say that it doesn’t seem as random as it’s supposed to be.

ACS

We own a house in a predominately-black, largely low income neighborhood, and we were asked to fill out a questionnaire for the American Crime Survey. Hm. And the request came with a crisp two dollar bill.

making maps in R

This map plots grocery stores with at least 30 employees, as of 2008. The map begins with a black and white Google terrain map and plots locations by latitude and longitude coordinates. I am working on getting more current data, but I can say that the food deserts haven’t changed much.

phlgrocer2008

Here’s the code I used. This requires the ggmap library.

qmap(“philadelphia”, zoom = 12, maptype=”terrain”, color=”bw”) +
geom_point(data=grocer, aes(x=long, y=latitude), colour=”blue”, alpha=.9,
size=4) +
labs(x=””, y=””, title=”Large Grocery Stores in 2008″) +
theme(plot.title = element_text(size=rel(1.8), face=”bold”, vjust=1.5,
family=”Times”), axis.ticks=element_blank(), axis.text.x=element_blank(),
axis.text.y=element_blank())