Hell will freeze over before the German government lets Deutsche Bank go bust.
They let Greece go bust rather than the Landesbank.
828 karma · joined January 23, 2014
They let Greece go bust rather than the Landesbank.
The UK government just won't have any input - that's a REAL loss of sovereignty.
If I use R for a plot, or a simple bit of regression, or anova, or even cross-validation. I don't reference it in a paper. I only cite it if there is a package designed for a particular type of data (e.g. a Bioconductor package) or something a bit more esoteric (e.g. apcluster). About 95% of the work is data munging and - sorry Hadley - I don't cite dplyr, purrr, magrittr etc...
However I have notice that in clinical trial or small social science papers simple analyses of this type are often cited as being done in SPSS or SAS. I think this just reflects the fact that non specialist data analysts are more likely to cite SAS or SPSS for simple procedures such as graphs or anova as an appeal to authority.
So I reckon the data may reflect a trend but tells us little about the true levels.
Plus dplyr essentially allows you to treat tables in a database as if they are data.frames.
'Trigger' I find annoying in almost every context; with the exception of facilities for the clinically traumatised I abhor the idea.
'Victim Blaming' is a useful term but I'm sure overused.
'Social Justice' seems more often these days sarcastically used as in 'Social Justice Warrior'
'Perpetuating false narratives' does seem just a pompous way of saying lies/propaganda.
... but returning to 'Privilege' I think that's a term that is overused - and annoyingly as in 'check your privilege' can be used to shut down a conversation or deny a viewpoint because of who someone is. A pernicious form of ad hominem.
And yet reading the article I think the argument is well made and the term 'privilege' is not used glibly. So perhaps it's unfair to write off the point just because that one word is used?
I also worry greatly about diagnostic predictive models that maximise overall prediction success but don't balance the relative consequences of false positives and false negatives.
"On Sunday, Northpointe gave ProPublica the basics of its future-crime formula — which includes factors such as education levels, and whether a defendant has a job. It did not share the specific calculations, which it said are proprietary."
How on earth can you lock people up based on secret information? That is Kafka meets Minority Report.
Software + Research = Chaos
Second "when some people get a lot richer" this especially pushes up the cost of housing (arguably education too) a rationed good. The experience here in the UK is that more and more people are renting from those who have the money to corner the housing market. A vicious cycle of money being redistributed upwards.
No doubt type 1 and type 2 poverty are linked.
The poorly paid actually spend a much higher % of there income in tax than the top 1%. And their contribution has been rising as the income and wealth based taxes are cut and the consumption based taxes have risen.
I don't blame you for thinking that is the case though as this canard has been pushed heavily by the UK press.
FIFA EA - Dribbling Ability: 23 (subjective)
Moneyball - On Base Percentage 23% (objective)
As Eisen points out its kind of odd that the group who rush to do the obvious human implementation should then get a share of all this potential... Not really a novel invention by that point in my opinion.
And of course whilst secret it leaves Google open to manipulate their UK sales too - as whilst they clam some Billions in their UK accounts in their US accounts they claim many Billions more.
With their now secret formula the UK public will never be able to pin them down when questioning what they are paying.
With a database of all communications you have to mine it for probabilistic leads. The signal to noise problem grows as does the database.
Someone else (I cant recall who) said - the last thing you need when looking for a needle in a haystack is more hay.
Either a free market fundamentalist (ie no explotative patents) or a dyed in the wool regulator should be able to agree this is not how the market should work.
A complex issue no doubt - but I can't stand opinions dressed as "sciencey" research.
Or even if you think you know unix/bash and data there are new and unexpected snippets every few pages that surprise you.
And a lot of chasing GDP or national wealth or Educational policy sometimes seem to be maximising some goal - like a game- rather than just running countries for the benefit of people.
I think its actually a very telling analogy if you think a little more deeply about it.
e.g. see for instance this discussion http://www.economist.com/blogs/freeexchange/2013/02/labour-m...
Further I can't really stand the crocodile tears of business who want to employ workers at below living level wages. Their fake concern for worker is nauseating.
If workers are having to top up wages with food stamps and government aid then the business, management, and shareholders rather than the worker are effectively being subsidised by the taxpayer.
And if you let companies lower wages to compete then the bad employers drive out the good. Far better to make them compete on the product or service not how badly they are willing to treat people.
However ecology is much cheaper than mol. biology. Essentially its ecology = manpower vs mol. biology = manpower + expensive technology + expensive chemistry.
Also ecology has been one of the most resistant fields to sharing data whereas genomics (the big data end of mol. biology) has been the most enthusiastic. There was a vey noticeable divide in opinion on twitter when PLOS journals mooted making authors make all their data public.
I think this is precisely because ecologists invest so much on collecting their field data, so they become possessive. Ecologists can become very senior PI by essentially owning a long term ecological study and assigning their students to continuing it.
Genomics and to a lesser extent Mol. Biol. has been keener recently to share as there is a strong open source ethos coming in from the computing side - and it is often a very expensive multi-centre, multi-agency collaboration.
Its unpredictable as it's all down to how the Labour and previous non-voters fall.
Journal referees will set the bar for such findings very very high...and you are quite likely to be refereed by the original author or their friends.
I know this to my cost having gone to extraordinary lengths to publish something slightly contradictory in a far lower impact journal than the work deserved.
However looking in the abstract doesn't really address either of these .. as even absent either P hacking or positive bias you would still expect the abstract to contain the selected highlights (i.e. positive findings) from the paper. It is the bit of the paper where you really should have positive selection bias!
If the paper has lots of negative tests (and most biological papers will report lots of negative control p values) these aren't picked up. A better way to see this problem (and I believe it is a problem) is to look at the whole paper and view the peak of margin results) compared to the whole set of p values.
it might or might not be a good goal for some conditions.