Anonymous wrote:Now Perry Stein at the Post has picked this up.
https://www.washingtonpost.com/local/education/dc-urban-moms-school-segregation-study/2021/03/31/8320b6e4-9160-11eb-a74e-1f4cf89fd948_story.html
And, great... she's quoting DCUM posts. Sigh.
I wish she had covered some of the actual substantive criticism here of the report.
Anonymous wrote:jsteele wrote:Anonymous wrote:jsteele wrote:Anonymous wrote:yes, exactly. A lot of the books that have come out this year (How to Be Anti-racist, Caste) are about exactly this. It's really understandable that people get defensive when they think they are being "called a segregationist." this year has been a long journey of trying to get people to face instead that they are participating in a racist system. it's a subtle difference but maybe one that can relieve some of that defensiveness.
To an extent I agree with you and I acknowledge that I probably should have viewed things more in this light. However, with regard to this report, I think its research is extremely shoddy, doesn't support the conclusions, and both ignores and reveals the obvious. Because the research is so light and flawed, what stands out are the allegations that are repeated throughout the report about supporting segregation. Perhaps the authors could have made their point without using such a loaded term? Is there really any justification for using such a term toward people who have chosen to remain in DC public schools rather than fleeing for private or the suburbs? Why antagonize the very folks with whom you must partner to find a solution?
Jeff, this reaction is white fragility in action. You can do better.
You may be correct that it is white fragility, but it is also reality. If people are interested in hard truths, it is a simple fact that this sort of language alienates your most likely allies. Why accuse people who didn't choose private schools and who didn't flee to the suburbs of supporting segregation? What solution does that help achieve?
This sounds like whataboutism, with respect to those who moved to the suburbs or choose private.
Should the authors not even bother to do this sort of research, for fear of how it will land with some defensive people? Hopefully for those that react less defensively, or move from initial defensiveness to actually thinking about whether they can do anything better, there will be a positive impact.
Anonymous wrote:Anonymous wrote: 'Scrape a website, do some analysis' is the correct way to go at all kinds of questions.
Oh, I agree.
Specifically, though: 'Scrape a website, count words' is NOT the correct way to get to this question or anything related to it. Seriously, Claude Shannon was building (not that useful) Markov models of word context in 1948, and those went beyond counting words. In this case some more cutting-edge text analysis might have actually been helpful.
But also, the questions are bad. It's both.
Anonymous wrote: 'Scrape a website, do some analysis' is the correct way to go at all kinds of questions.
jsteele wrote:Anonymous wrote:Haven’t seen Jeff in awhile, so I imagine him at a desk typing madly away with purpose. His response could become even longer than the Brookings report. Don’t do it, Jeff! Save yourself!!
Hopefully though he’s stepping away a bit to process things.
Nope. First, I never work at a desk. Second, I just spent an hour split between my rower and exercise bike. I'm back, I'm ready, and my arms and legs are tired.
Anonymous wrote:Also here is a tutorial that shows how to do sentiment analysis.
With open-source tools (i.e. free), and in realtime (i.e. much much harder than analyzing a big pile of static data you scraped and writing a report.)
https://towardsdatascience.com/real-time-sentiment-analysis-on-social-media-with-open-source-tools-f864ca239afe
The word embedding algorithm takes as its input from a large corpus of text and produces these vector spaces, typically of several hundred dimensions. A neural language model is trained on a large corpus (body of text) and the output of the network is used to each unique word to be assigned to a corresponding vector. The most popular word embedding algorithms are Google ‘s Word2Vec, Stanford ‘s GloVe or Facebook ‘s FastText.
Word embeddings represent one of the most successful AI applications of unsupervised learning.
This is well-trodden ground by now.
The word embedding algorithm takes as its input from a large corpus of text and produces these vector spaces, typically of several hundred dimensions. A neural language model is trained on a large corpus (body of text) and the output of the network is used to each unique word to be assigned to a corresponding vector. The most popular word embedding algorithms are Google ‘s Word2Vec, Stanford ‘s GloVe or Facebook ‘s FastText.
Word embeddings represent one of the most successful AI applications of unsupervised learning.
Anonymous wrote:Out of curiosity, is there anyone who believes the actual academic scholarship done here (word frequency analysis without contextual controls) qualifies as good scholarship? I'm not sure I have seen a Brookings-level report before where the discussion about the publication is so unanimous in agreement that the underlying methodology is significantly flawed. Maybe I am missing something, though.
This tutorial aims to cover the basic motivation, ideas, models and learning algorithms in deep learning for natural language processing.
Recently, these methods have been shown to perform very well on various NLP tasks such as language modeling, POS tagging, named entity recognition, sentiment analysis and paraphrase detection, among others.
Anonymous wrote:Haven’t seen Jeff in awhile, so I imagine him at a desk typing madly away with purpose. His response could become even longer than the Brookings report. Don’t do it, Jeff! Save yourself!!
Hopefully though he’s stepping away a bit to process things.