Welcome Lovelies!

tastefullyoffensive:

The best costume spotted at Disneyland’s 10k race today. [adamlc6]

tastefullyoffensive:

The best costume spotted at Disneyland’s 10k race today. [adamlc6]

r-emnant:

i went shopping for school supplies yesterday

(via skinnykate)

rosalarian:

pourquoi-nutmeg:

nortonism:

The thing about this is that sculptures like these in art history were for the male gaze. Photoshop a phone to it and suddenly she’s seen as vain and conceited. That’s why I’m 100% for selfie culture because apparently men can gawk at women but when we realize how beautiful we are we’re suddenly full of ourselves…

YES.

Girls don’t let anyone tell you loving yourself is vanity.

rosalarian:

pourquoi-nutmeg:

nortonism:

The thing about this is that sculptures like these in art history were for the male gaze. Photoshop a phone to it and suddenly she’s seen as vain and conceited. That’s why I’m 100% for selfie culture because apparently men can gawk at women but when we realize how beautiful we are we’re suddenly full of ourselves…

YES.

Girls don’t let anyone tell you loving yourself is vanity.

(Source: nevver, via skinnykate)

lifeanddragons:

If there is possibly anything more profound.

lifeanddragons:

If there is possibly anything more profound.

(via h-spells)

adubs132:

well shit. voldemort is now trying to take over one of the districts in the hunger games. what is this?

adubs132:

well shit. voldemort is now trying to take over one of the districts in the hunger games. what is this?

(Source: fuckyeahpotterphotography, via mundanemerman)

screw stardust; be iron instead.
be the element that creates stardust.
be the element that causes the largest stars to explode.
be the element that is strong enough to collapse an entire universe.”
-k.m | supernovae (via lastisle)

(Source: silverlinedmemories, via b-undt)

sinidentidades:

The racist immigrants carry disease rhetoric is nothing new. 

Perhaps we need a U.S. history lesson:

Under President Franklin D. Roosevelt, the U.S. forged a program, through a series of agreements with Mexico’s PRI-dominated government, called the Bracero program. This program was used to fill in the gaps in manual labor the U.S. had after the war.

It sounds like a liberal dream: immigrants being given an opportunity to work in the “land of opportunity,” yet it was hardly that. The laborers were forced into horrible working conditions. Many died from exhaustion (often from working in the sun too long) from working in the fields picking food for the U.S. Many also suffered from disease.

The U.S. decided what was best for the issue of disease: a widespread use of a highly toxic livestock pesticide that braceros were often doused in as part of processing into the U.S.

(via buymorialand)

vwcampervan-aldridge:

Ornate Gatehouse, Lyndhurst, New Forest, Hampshire, England
All Original Photography by http://vwcampervan-aldridge.tumblr.com

vwcampervan-aldridge:

Ornate Gatehouse, Lyndhurst, New Forest, Hampshire, England

All Original Photography by http://vwcampervan-aldridge.tumblr.com

(via coffee-in-europe)

allthingslinguistic:

superlinguo:

Fun times are on the up.
I’m not a corpus linguist, but I love playing with different corpora when they’re presented in accessibly and fun ways - so I was thrilled when Claire Hardaker tweeted about the NYT Chronicle, a way to visualise the language used across the newspaper’s history. 
Like Google’s n-gram corpus, it presents a nice clear chart. It has some advantages over n-gram, for example the NYT corpus is completely up to date while Google’s gets sketchy for contemporary references; compare NYT drone to n-gram drone and you see the NYT data kicks up swiftly just where the Google data ends. 
There are obviously biases in this data too. For one, there’s a bias towards American spelling that isn’t as pronounced in the Google Books corpus. The genre represented is also fairly narrow.
I found a nice use for it the other day while listening to a This American Life podcast that talked about “the meat question”; a period in the late 19th and early 20th century when the USA was unsure it would have enough viable agriculture to feed its population and looked at alternative sources of meat (including, most famously, hippopotamus). The NYT Chronicle has a nice couple of spikes in usages of this phrase when the issue was most pressing (and therefore made it into the news), while the Google Books usage is more diffuse, as people wrote books in the aftermath, being a corpus that is less immediate than newspapers.
This may not become my default go-to tool, but it’s nice and simple and makes a great point of comparison to n-gram. Thanks Claire for sharing!

I can imagine in the long term that if one compared, say, a future Twitter corpus that managed to make graphs like this, along with the NYT Chronicle and Google Ngrams, that the Twitter one would be even spikier because it’s not subject to editing or any time-delay at all. I really hope someone eventually makes a Twitter graphing feature like this now!
It’s interesting to see the same terms, such as boomer, baby boomer, millennial, generation x, generation y, give us quite different graphs in NYT Chronicle versus Google Ngrams. Both of them show increases, but for example there are spikes for “boomer” in NYT in 1994 (relating to a sports story) and for “millennial” in 1999 (relating to New Years) that are entirely absent from Ngrams. 

allthingslinguistic:

superlinguo:

Fun times are on the up.

I’m not a corpus linguist, but I love playing with different corpora when they’re presented in accessibly and fun ways - so I was thrilled when Claire Hardaker tweeted about the NYT Chronicle, a way to visualise the language used across the newspaper’s history. 

Like Google’s n-gram corpus, it presents a nice clear chart. It has some advantages over n-gram, for example the NYT corpus is completely up to date while Google’s gets sketchy for contemporary references; compare NYT drone to n-gram drone and you see the NYT data kicks up swiftly just where the Google data ends. 

There are obviously biases in this data too. For one, there’s a bias towards American spelling that isn’t as pronounced in the Google Books corpus. The genre represented is also fairly narrow.

I found a nice use for it the other day while listening to a This American Life podcast that talked about “the meat question”; a period in the late 19th and early 20th century when the USA was unsure it would have enough viable agriculture to feed its population and looked at alternative sources of meat (including, most famously, hippopotamus). The NYT Chronicle has a nice couple of spikes in usages of this phrase when the issue was most pressing (and therefore made it into the news), while the Google Books usage is more diffuse, as people wrote books in the aftermath, being a corpus that is less immediate than newspapers.

This may not become my default go-to tool, but it’s nice and simple and makes a great point of comparison to n-gram. Thanks Claire for sharing!

I can imagine in the long term that if one compared, say, a future Twitter corpus that managed to make graphs like this, along with the NYT Chronicle and Google Ngrams, that the Twitter one would be even spikier because it’s not subject to editing or any time-delay at all. I really hope someone eventually makes a Twitter graphing feature like this now!

It’s interesting to see the same terms, such as boomer, baby boomer, millennial, generation x, generation y, give us quite different graphs in NYT Chronicle versus Google Ngrams. Both of them show increases, but for example there are spikes for “boomer” in NYT in 1994 (relating to a sports story) and for “millennial” in 1999 (relating to New Years) that are entirely absent from Ngrams.