I really am at cutting edge… authenticity comfort zones…

A couple days ago on the blog I remarked on the issue of authenticity in my short review of Tarquin Hall’s Indian detective story. I wish I could say I inspired Tyler Cowen of Marginal Revolution to then do the work of finding an academic study… but I think it was just coincidence that he posted a link to this study.

We present two studies that together test a fundamental yet rarely examined assumption underlying the contemporary appeal of authenticity—namely, that consumers assign higher value ratings to organizations regarded as authentic. Study 1 conducts content analysis of unsolicited online restaurant reviews entered voluntarily by consumers in three major U.S. metropolitan areas from October 2004 to October 2011; the data contain information from 1,271,796 reviews written by 252,359 unique reviewers of 18,869 restaurants. The findings show that consumers assign higher ratings to restaurants regarded as authentic, even after controlling for restaurant quality in several ways. In addition, we find that consumers perceive independent, family-owned, and specialist single-category restaurants as more authentic than they do chain, non-family-owned, and generalist multiple-category restaurants. Study 2 reinforces these findings using an experimental design in which participants were presented with photos and minimal descriptions of fictitious restaurants and then asked to evaluate the likely authenticity, quality, and overall value of the restaurants in a predetermined sequence. Central to both studies is an authenticity scale that was developed through the use of an online survey that ascertains the specific language used by individuals in referencing authenticity in the restaurant domain. Taken together, these studies demonstrate that authenticity generates higher consumer value ratings of organizations; the studies also identify certain types of organizations that are more likely to receive authenticity attributions by consumers.

via Organization Science: INFORMS.

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“Après la nuit” de Basil da Cunha

Hope it comes to Netflix soon… looks interesting.

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France: 70 years ago women get the right to vote

Le 21 avril 1944, les femmes françaises accèdent au droit de vote. Elles ne pourront cependant l’exercer pleinement qu’à partir du 29 avril 1945, date des élections municipales, premier scrutin d’après-guerre.

Il y a seulement 70 ans, en vertu de l’ordonnance d’Alger du gouvernement provisoire du général de Gaulle, les femmes obtiennent le droit de voter et d’être élues. Dans les images d’archives en noir et blanc, la voix est nasillarde et le commentateur des actualités filmées présente fièrement la « bonne religieuse » et une « gentille maman » glissant leur bulletin dans l’urne.

Leur toute nouvelle carte électorale en poche, les Françaises votent pour la première fois le 29 avril 1945 lors du premier tour des élections municipales, premier scrutin depuis la Libération. Ce 29 avril, les Françaises se sont déplacées en masse. On enregistre dans plusieurs villes de France des taux de participation féminine identiques à ceux des hommes.

via France: il y a 70 ans, les femmes obtenaient le droit de vote – france – RFI.

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Akhil Sharma’s “We Didn’t Like Him” in The New Yorker

I found this quite a good story.  Although told in a very straightforward style, with no verbal pyrotechnics, one senses early on that there is a profound ambiguity in the relationship between narrator and his distant relative Manshu.  A clever aspect of the story, quite deliberate, is that the reader never really gets into Manshu’s head.  We have no idea what the adult Manshu is thinking.  The inarticulable feelings of the narrator deepen over time.  I think the story is partly about how an emotion like “liking” or “not liking”changes and is nuanced over time, and how that sentiment can suddenly shift through an experience of intimacy (in the sense of proximity and aloneness and action).  As a moral tale it works quite well, though it is a tiny bit contrived.

As usual The Mookse and the Gripes had a great discussion. An excerpt from Betsey:

I liked Akhil Sharma’s “We Didn’t Like Him,” whose setting is the land of death and what we do to relieve its loss. The story felt so appropriate to the day, given that I was reading it on Memorial Day, a time in my own family which had often been marked by visits to the cemetery bearing flowers. Just to think of the cemetery, though, one is confronted with confusion: so much lost, so much undone. Sharma’s story floats on these facts – what scarcities life provides us with to deal with the deprivations death enforces. Sharma’s flat tone allows him to tell both about the scarcities that death ensures, and also about the kind of sudden reversal into life and newness that we all crave.

via Akhil Sharma: “We Didn’t Like Him” | The Mookse and the Gripes.

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Running a regression in R and formatting output

# Thanks to Bill Sundstrom for the mtable formatting code

# input data into R
data <- read.table(“tenureinsecurity.csv”, header=TRUE, sep=”,”)

# Tell R to assume survey1 is dataset from now until detach
attach(data)

# Run two regressions
fit1 <- lm(yield ~  female+age+fieldsize1)
fit2 <- lm(yield ~  female+age+fieldsize1+tenure)

# Put regression results into a table and label appropriately
fitx <- mtable(“Model 1″=fit1,”Model 2″=fit2,summary.stats=c(“sigma”,”R-squared”,”F”,”p”,”N”) )
(fitx <- relabel(fitx,
“(Intercept)” = “Constant”,
tenure = “Tenure insecurity”,
fieldsize1 = “Field size – hect.”,
female = “Is female?”,
age = “Age of farmer”
))

# Produces output in tab-delimited format
# Notepad window opens up in Windows, in Mac an R window opens; The contents can be pasted into Word or Excel
file123 <- “mtable123.txt”
write.mtable(fitx,file=file123)
file.show(file123)

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Creating bar plots and box plots of variable by several categories (factors) in R

# input data into R
readincamp = read.csv(“competitivereadingcamp.csv”)

# Tell R to assume readincamp is dataset from now until detach
attach(readincamp )

# Summarize data with summarySE command
# Source; the excellent http://www.cookbook-r.com/Graphs/Plotting_means_and_error_bars_%28ggplot2%29/
# To do this have to install.packages(“bear”)
# summarySE gives count, mean, standard deviation, standard error of the mean, and confidence interval (default 95%).
# rc2 will be a new data frame.
# measurevar: the name of a column that contains the variable to be summariezed
# groupvars: a vector containing names of columns that contain grouping variables
# na.rm: a boolean option that indicates whether to ignore NA’s – missing values
# conf.interval: the percent range of the confidence interval (default is 95%)
install.packages(“bear”)
library(bear)
rc2 <- summarySE(readincamp, measurevar=”score1″, groupvars=c(“treatment”, “female”))

# In the new data frame rc2, make treatment and female into factor rather than numeric variables
rc$treatment2 <- factor(rc$treatment)
rc2$female2 <- factor(rc$female)

# Now use ggplot to make the bar plot
# need install.packages(“ggplot2″) and then library(“ggplot2″)
# Error bars represent standard error (se) or confidence interval (ci) of the mean
ggplot(rc2, aes(x=treatment2, y=score1, fill=female2)) +
geom_bar(position=position_dodge(), stat=”identity”)  + # Thinner lines
geom_errorbar(aes(ymin=score1-ci, ymax=score1+ci),
size=.3,    # Thinner lines
width=.2,
position=position_dodge(.9)) +
xlab(“Treatment”) +
ylab(“Score 1″) +
scale_fill_hue(name=”Gender”, # Legend label
breaks=c(“0″, “1″),
labels=c(“Male”, “Female”)) +
ggtitle(“The Effect of Treatment on Test Score1 (with confidence intervals)”) +
scale_y_continuous(breaks=0:20*2) + #control ticks on y axis
theme_bw() #make  background white

# make a boxplot of the means
# Here is some great help http://www.r-bloggers.com/box-plot-with-r-tutorial/
boxplot(score1  ~ female*competitive, main=”Scores on reading test”,
xlab=””, ylab=”Score 1″,
col=(c(“white”,”gray”)), las = 2,
at =c(1,2,4,5),par(mar = c(8, 5, 4, 2)+ 0.1),
names = c(“men, noncomp”,”men, comp”,”women, noncom”,”women, comp”))

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Calculating means by categorical variables (factors) in R

# These are some of the different ways to do the calculation that social scientists probably
# do most frequently; Calculate means for difference groups or conditions
# input data into R (the data is on some reading camps run in Ghana)
readincamp = read.csv(“competitivereadingcamp.csv”)

# Tell R to assume readincamp is dataset from now until detach
attach(readincamp )

# Find the means of score1 initial scores, by gender and type of reading camp
summaryBy(score1  ~ female+competitive, data=readincamp , FUN=c(mean),na.rm=TRUE)
summaryBy(score1  ~ female+africanbooks, data=readincamp , FUN=c(mean),na.rm=TRUE)
# Another way to do it
print(tapply(X=score1, INDEX=list(africanbooks, female), FUN=mean , na.rm = TRUE))
print(tapply(X=score1, INDEX=list(competitive, female), FUN=mean , na.rm = TRUE))

# Another way: Summarize data with summarySE command

# Source: the excellent http://www.cookbook-r.com/Manipulating_data/Summarizing_data/
# To do this have to install.packages(“bear”)
# summarySE gives count, mean, standard deviation, standard error of the mean, and confidence interval (default 95%).
# rc2 will be a new data frame.
# measurevar: the name of a column that contains the variable to be summariezed
# groupvars: a vector containing names of columns that contain grouping variables
# na.rm: a boolean option that indicates whether to ignore NA’s – missing values
# conf.interval: the percent range of the confidence interval (default is 95%)
install.packages(“bear”)
library(bear)
rc2 <- summarySE(readincamp, measurevar=”score1″, groupvars=c(“treatment”, “female”))

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