Archive for Language of science

Science Wars

The dark side of Paul Feyerabend's anarchistic philosophy of science — the observation that researchers often try to resolve scientific questions by political means, in the most negative possible sense of "political" — is confirmed all too often by experience. Linguists can point to the excesses of the The Linguistics Wars of the 1970s, and to plenty of other doctrinal disputes, including the Great Recursion Squabble of recent memory. So it's paradoxically a comfort to read Bianca Bosker's article "The nastiest feud in science", The Atlantic 9/2018, about Gerta Keller's struggle to argue that the Fifth Extinction was caused by volcanos rather than by a meteor:

The impact theory provided an elegant solution to a prehistoric puzzle, and its steady march from hypothesis to fact offered a heartwarming story about the integrity of the scientific method. “This is nearly as close to a certainty as one can get in science,” a planetary-science professor told Time magazine in an article on the crater’s discovery. […]

While the majority of her peers embraced the Chicxulub asteroid as the cause of the extinction, Keller remained a maligned and, until recently, lonely voice contesting it. She argues that the mass extinction was caused not by a wrong-place-wrong-time asteroid collision but by a series of colossal volcanic eruptions in a part of western India known as the Deccan Traps—a theory that was first proposed in 1978 and then abandoned by all but a small number of scientists. Her research, undertaken with specialists around the world and featured in leading scientific journals, has forced other scientists to take a second look at their data.[…]

Keller’s resistance has put her at the core of one of the most rancorous and longest-running controversies in science. “It’s like the Thirty Years’ War,” says Kirk Johnson, the director of the Smithsonian’s National Museum of Natural History. Impacters’ case-closed confidence belies decades of vicious infighting, with the two sides trading accusations of slander, sabotage, threats, discrimination, spurious data, and attempts to torpedo careers. “I’ve never come across anything that’s been so acrimonious,” Kerr says. “I’m almost speechless because of it.” Keller keeps a running list of insults that other scientists have hurled at her, either behind her back or to her face. She says she’s been called a “bitch” and “the most dangerous woman in the world,” who “should be stoned and burned at the stake.”

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Hurricane naming policy change

I think it's becoming clear that alternating male and female personal names to individuate Atlantic tropical cyclones is not a good idea. These storms are becoming far too nasty. Calling a storm "Harvey" makes it sound like your friendly uncle who always comes over on the Fourth of July and flirts with your mom. And "Irma" sounds like a dancer that he once knew when he was in Berlin. Science tells us that these devastating meteorological events are probably going to get worse in coming years. (Ann Coulter says that as a potential cause of increased violence in hurricanes, climate change is less plausible than God's anger at Houston for having elected a lesbian mayor; but let's face it, Ann Coulter is a few bricks short of a full intellectual hod.) Hurricanes need uglier names. You can't get Miami to evacuate by telling people that "Irma" is coming.

Accordingly, next year the National Hurricane Center is planning to name tropical cyclonic storms and hurricanes after unpleasant diseases and medical conditions. Think about it. The state governor tells you a hurricane named Dracunculiasis is coming down on you, you're gonna start packing the station wagon. So as the season progresses, the following will be the named storms in 2018.

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Dr. Dolittle springs eternal

Nicola Davis, "Bat chat: machine learning algorithms provide translations for bat squeaks", The Guardian 12/22/2016

It turns out you don’t need to be Dr Doolittle to eavesdrop on arguments in the animal kingdom.  

Researchers studying Egyptian fruit bats say they have found a way to work out who is arguing with whom, what they are squabbling about and can even predict the outcome of a disagreement – all from the bats’ calls.  

“The global quest is to understand where human language comes from. To do this we must study animal communication,” said Yossi Yovel, co-author of the research from Tel Aviv University in Israel. “One of the big questions in animal communication is how much information is conveyed.”

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On Thursday and Friday, I participated in a workshop on"Statistical Challenges in Assessing and Fostering the Reproducibility of Scientific Results" at the National Academy of Sciences in Washington DC.

Some of the presentations were even more horrifying than I expected — at one point, an audience member was moved to ask half-seriously whether ANY reproducible result has ever been published in biomedical research — but others described positive trends and plans.

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"X percent of Y are Z"

It's amazing how troublesome simple percentage-talk can be. Donald McNeil Jr., "Fewer Ebola Cases Go Unreported Than Thought, Study Finds", NYT 12/16/2014

By looking at virus samples gathered in Sierra Leone and contract-tracing data from Liberia, the scientists working on the new study estimated that about 70 percent of cases in West Africa go unreported. That is far fewer than earlier estimates, which assumed that up to 250 percent did.

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When half of a quarter is all, or at least mostly

Steve Connor, "Nature rather than nurture governs intelligent behaviour in primates, scientists discover", The Independent 7/10/2014:

The vexed question of whether intelligence is inherited from birth or acquired through education seems to have been answered – for chimpanzees at least.

Scientists have found that being a smart primate is down to genes rather than upbringing, suggesting that nature rather than nurture governs intelligent behaviour in our closest living relatives.

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North, south, east, west

Bill Watkins, who teaches Chinese and science at a small independent high school near Baltimore, asks three semi-related questions about directional words.

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Nicholas Wade: Genes, culture, and history

Nicholas Wade never met a genetic just-so story that he didn't like. For a partial survey, see "The hunt for the Hat Gene", 11/15/2009, where I observed that he pivots smoothly from mere over-interpretation to complete fabrication:

Nicholas Wade is an inveterate gene-for-X enthusiast — he's got 68 stories in the NYT index with "gene" in the headline — and he's had two opportunities to celebrate this idea in the past few days: "Speech Gene Shows Its Bossy Nature", 11/12/2009, and "The Evolution of the God Gene", 11/14/2009. The first of these articles is merely a bit misleading, in the usual way. The second verges on the bizarre.

Now Mr. Wade has packaged a large-scale version of this move as a book, where a somewhat tendentious account of human genetic diversity transitions into a fictional narrative proposing genetic explanations for essentially every aspect of human cultural, social, and economic history: A Troublesome Inheritance: Genes, Race and Human History, 2014.

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Wallflower no more

Yesterday afternoon at an ICASSP-2014 session on music transcription. Just as in the session on Diarization that I wrote about yesterday, most of the papers reported results on published data, and several also offered links to their code. Thus Ken O'Hanlon and Mark D. Plumbley, "Polyphonic Piano Transcription using Non-negative matrix factorisation and group sparsity", which ends with this note:

This research benefits from the efforts of other researchers to share their code [5] and dataset [21]. The open availability of these resources is commendable, allowing other researchers to easily and accurately compare methods. The code used in the experiments described in this paper is available at bnmf/.

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When 90% is 32%

I've occasionally complained that when it comes to comparing sampled distributions, modern western intellectuals are mostly just as clueless as the members of the Pirahã  tribe in the Amazon are said to be with respect to counting (see e.g.  "The Pirahã and us", 10/6/2007).  And it doesn't take high-falutin concepts like "variance" or "effect size" to engage this incapacity — simple percentages are often enough.

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(Mis-) Interpreting medical tests

Jon Hamilton, "Alzheimer's Blood Test Raises Ethical Questions", NPR Morning Edition 3/9/2014:

An experimental blood test can identify people in their 70s who are likely to develop Alzheimer's disease within two or three years. The test is accurate more than 90 percent of the time, scientists reported Sunday in Nature Medicine.

The finding could lead to a quick and easy way for seniors to assess their risk of Alzheimer's, says Dr. Howard Federoff, a professor of neurology at Georgetown University. And that would be a "game changer," he says, if researchers find a treatment that can slow down or stop the disease.  

But because there is still no way to halt Alzheimer's, Federoff says, people considering the test would have to decide whether they are prepared to get results that "could be life-altering." 

But  having a prediction with no prospect for a cure is not, in my opinion, the biggest problem with tests of this kind.

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The inclusion epidemic

Last week, a journalist asked me a question in connection with the recent flurry of stories on changes in childhood obesity percentages in the period from 2001 to 2012. When I looked into it, what struck me was that a category defined as "BMI at or above the 95th percentile" applied to about 15-17% of the population throughout the period discussed.

This sounds like a statistical approximation to Garrison Keillor's joke about his home town, where "all of the children are above average". But the normative percentiles are based on data from an earier time, and so it's perfectly logical that 17.1% of the age-2-19 sample in the 2003-2004 period should be at or above the 95th percentile for the 1963-1994 period. This is just a symptom, after all, of the famous "obesity epidemic".

Still, I remained curious about just when this large change really took place. (Most of) the raw data is available on line from the CDC, and I decided to spend an hour or so satisfying my curiosity about what is going on here: has there actually been a gradual climb over 50 years, which looks steep when a threshold derived from 1963-1994 is used for data from 2003 to the present? Or was there a steeper climb over a narrower stretch of time?

I found a clear answer to this question. But when I looked into it further, I found some additional information that made me wonder whether there has really been any change over time at all.

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(Not) trusting data

Pete Warden, "Why you should never trust a data scientist", 7/18/2013:

The wonderful thing about being a data scientist is that I get all of the credibility of genuine science, with none of the irritating peer review or reproducibility worries. […]

I’ve never ceased to be disturbed at how the inclusion of numbers and the mention of large data sets numbs criticism. The articles live in a strange purgatory between journalism, which most readers have a healthy skepticism towards, and science, where we sub-contract verification to other scientists and so trust the public output far more. If a sociologist tells you that people in Utah only have friends in Utah, you can follow a web of references and peer review to understand if she’s believable. If I, or somebody at a large tech company, tells you the same, there’s no way to check. The source data is proprietary, and in a lot of cases may not even exist any more in the same exact form as databases turn over, and users delete or update their information. Even other data scientists outside the team won’t be able to verify the results. The data scientists I know are honest people, but there’s no external checks in the system to keep them that way.

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