Language Log

A new political advertisement, apparently from the campaign of Kelly Loeffler:

An interesting malapropism:

Seen on the internet:

From @togetherfightingdisease on Instagram:

View this post on Instagram

A post shared by 🇭🇰 | 一齊打怪獸💛 (@togetherfightingdisease) on Sep 6, 2020 at 9:43pm PDT

Final panel of this New York Times article:  "What You Can No Longer Say in Hong Kong" (9/4/20), by Jin Wu and Elaine Yu:

The speaker of the Czech senate addresses Taiwan's parliament alluding to JFK's ‘Ich bin ein Berliner’ — and finishes by saying "Wǒ shì Táiwān rén 我是台湾人" ("I am Taiwanese") in (an attempt at) Mandarin.

The video is in Czech but it's easy to spot where this happens — right before the ovation — at 1:12.

The video is embedded here: 

"Vystrčil na Tchaj-wanu připomněl slavná slova Kennedyho. Dočkal se potlesku ve stoje", Zahraničí

You may have to watch through several ads in Czech.  It's fun to listen to them.

From John Berenberg:

The following graphics reflect the disgust of Hong Kong protesters over the police rewriting of the notorious attack on subway passengers by CCP orchestrated goons at the Yuen Long MTR station on July 21, 2019 (hence "721").

All of the illustrations have as their theme the set phrase (chéngyǔ 成語, often misleadingly referred to as "idioms") zhǐlùwéimǎ 指鹿為馬 ("point at a deer as a horse", i.e., "point at a deer and call it a horse"), i.e., "deliberate misrepresentation for ulterior purposes".

The Records of the Grand Historian records that [the powerful eunuch] Zhao Gao [d. 207 BC], in an attempt to control the Qin [221-206 BC] government, devised a loyalty test for court officials using a deer and horse:

Zhao Gao was contemplating treason but was afraid the other officials would not heed his commands, so he decided to test them first. He brought a deer and presented it to the Second Emperor but called it a horse. The Second Emperor laughed and said, "Is the chancellor perhaps mistaken, calling a deer a horse?" Then the emperor questioned those around him. Some remained silent, while some, hoping to ingratiate themselves with Zhao Gao, said it was a horse, and others said it was a deer. Zhao Gao secretly arranged for all those who said it was a deer to be brought before the law and had them executed instantly. Thereafter the officials were all terrified of Zhao Gao. Zhao Gao gained military power as a result of that. (tr. Watson 1993:70)

(Source)

"Straight talk on the FDA’s tumultuous weekend — and new questions about its independence", Stat 8/24/2020:

Matt Herper: So for those just back from a tour of Jupiter’s moons, last night the FDA granted emergency use authorization of convalescent plasma to treat patients with Covid-19. Trump characterized the decision as a major breakthrough. FDA Commissioner Stephen Hahn, who joined him at a news conference to announce the decision, backed him up — but he also misspoke, claiming that giving plasma would help 35 out of 100 people treated.

Adam Feuerstein: Misspoke is being kind. Hahn grossly mischaracterized the benefit of convalescent plasma on Sunday night. I’ll just quote him here: “A 35% improvement in survival is a pretty substantial clinical benefit. What that means is — and if the data continue to pan out — 100 people who are sick with Covid-19, 35 would have been saved because of the administration of plasma.” […]

Matt: That number should be at best 5 out of 100 people. To my eye, it’s more like 3 out of 100 people. And all that is from subgroups of an observational study, so it should be taken with a grain of salt.

Researchers didn’t compare patients who got plasma to a control group. They compared those who got the drug early to those who got it late, and between high levels of antibodies in the plasma and low ones. For the main subset in the study, which was led by the Mayo Clinic, mortality at seven days was 11% for those who got lots of antibodies, versus 14% for those who got few. That’s three out of 100 — again, with a grain of salt.

Not sure what they mean by "grammar" here, but they sure do have a message:

Cai Xia 蔡霞, a retired female professor from the Central Party School of the CCP has been denouncing Xi Jinping for his imperial aspirations and the CCP as a corrupt, zombie party.  Somehow, she managed to escape to the United States after her initial condemnations.

Fuming, the Party has cancelled her membership and vilified her perfidy:

After the Party School of the Central Committee of Communist Party of China (CPC) announced on Monday that it had rescinded the Party membership of retired professor Cai Xia and revoked her retirement benefits, Cai quickly became Western media's blue-eyed person.

Source:  "Cai Xia’s blatant betrayal is totally indefensible: Global Times editorial", Global Times* (8/19/20)

*An official CCP daily tabloid sponsored by the People's Daily.

[Update — apparently the data for the graphs presented by Sabeti and Miller came originally (without attribution) from work by David Rozado, who has provided useful information about his sources and methods. I therefore withdraw the suggestion that the counts were wrong, pending further study, though I am still not persuaded by the arguments that Sabeti and Miller used their version of his graphs to make.]

This is the subgraph for "racism" from the display originally presented in John F Miller's 2019 tweet, reproduced a few days ago by Arram Sabeti, and allegedly representing "New York Times Word Usage Frequency (1970 to 2018)":

Earlier today ("Sabeti on NYT bias"), I lodged some objections to Miller's graphs, especially the way that the y-axis scaling misrepresents the relative frequency of the various words and phrases covered. But after looking into things a little further, I find that it's not just a scaling problem — the underlying number sequences in Miller's graphs are substantially different from what I find in a search of the NYT archive, at least in the cases that I've checked. I don't know whether this is because of some issue with Miller's numbers, or with the counts from the NYT archive, or what. But for whatever reason, Miller's numbers are (in all cases where I've checked) seriously at variance with the results of NYT archive search.

And the differences make a difference — Miller's tendentious conclusion that "social liberal media and academia are wilfully gaslighting people" is even less well supported by the Archive's numbers than it was by the original misleadingly-scaled graphs.

Barbara Partee asked me to comment on this thread by Arram Sabeti — crucial bit here:

2/ The data makes it plain that the NYT has abandoned its commitment to nonpartisan reporting. When the internet threatened their business they made a devil’s bargain to amplify outrage and us-vs-them psychology. Racism wasn't a new problem in 2014 but their stock being down was. pic.twitter.com/R7s7E7Mc4H

— Arram Sabeti (@arram) July 28, 2020