Congratulations Yuzuru for getting married I'm so happy for you I hope your marriage lasts a long while and people won't disturb you or your S/O I also hope your resting more than you used too when you figure skated<3
"everyone who supports us
Thank you for always supporting me.
This time I, Yuzuru Hanyu, will be registered
For the past 24 years, I have lived with skating. In particular, over the past few years, in a world that has been unstable and rapidly changing due to COVID-19, natural disasters, and the world's situation, I have felt many things, faced skating, and have learned various things about myself and the world, I kept thinking. Even though I'm an immature person, I have received immeasurable strength from everyone's support, expectations and, gazes.
Thank you very much.
Even today, I will dedicate my life to deepening the skating of "Hanyu Yuzuru", continue to work hard and, evolve.
From now on, I will continue to build up every moment, one by one, so that I can skate the best.
For the rest of my life, along with everyone who supports me and skating, I will do my best to move forward and live.
And with gratitude to those who have supported me and those who will continue to support me, I will continue skating so that everyone can be in the best shape. Thank you for your continued support.
August 4, 2023
Yuzuru Hanyu"
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@difeisheng has awoken some huáyû 華語* translation pet peeves of mine with this post and compelled the need to vent a little.
知己 has been translated to "soulmate" in subtitles, which -I understand- is short and convenient, and the (cdrama) fandoms ran with that, as fandoms do, bless them!
BUT
"Intimate friend" would be a more accurate, although I don't actually really mind the translation as " soulmates", it just lack nuances and the weight of amatonormativity too often reduces theses words to romantic (and carnal) bonds.
知己
to know - self: a person who understands me (sometimes better than myself).
紅粉知己
rouge - powder - to know - self: a (platonic) lady-best-friend to a man.
知音
to know - sound: a person who understands my tunes/melodies. An artistic soulmate bond, can be between an artist and a connoisseur or people who collaborate to create art, like a musician and a lyricist, two writers, a writer and a graphic artist... (I made a MDZS/The Untamed piece a while ago and already felt the need to explain).
Since we're at it:
緣,緣分
It's the karmic predestined affinity. Between people it can be any connection/relationship: family, friends, rivals even enemies. Without 緣 we would merely be strangers passing by each other.
姻緣
It's a type of 緣分 that leads to matrimony. The red thead that the matchmaker 月下老人 "Old Man Under the Moon" or 月老 YuèLâo ties to the pinkies of a predestined pair, a man and a woman, because of patriarchal heteronormativity but I know (at least in Taiwan) that anyone nowadays can ask for a good romantic relationship in a temple. (Some shrines to the 兔爺兒神 Rabbit deity for same gender relationships but it's not as widely known or accessible).
*instead of Chinese/Mandarin/HànYû choose the term 華語 Huáyû because it's what covers the best the diversity of the diaspora and learning speakers, without geographical/historical/ethnical restrictions.
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Chinese Surname Ranking Analysis
According to this list, these are the top 10 surnames in China as of 2020*:
李 Lǐ
王 Wáng
张 Zhāng
刘 Liú
陈 Chén
杨 Yáng
赵 Zhào
黄 Huáng
周 Zhōu
�� Wú
I’ve seen articles like this one discussing the regional distribution of surnames. I wanted to take a look myself and compare the top surnames in different provinces/municipalities. For instance, do any locations have a top 10 list that is the same as the overall country top 10? Which surname is ranked #1 in the most locations? Let’s explore and have some fun along the way.
*The top surnames for 西藏 Tibet are not included. I believe this is because the population is vast majority Tibetan.
1) Find how many surnames in a location’s top 10 are also in the national top 10.
Most in common
Arrows: grey = same as national rank, green = higher than national rank, red = lower than national rank
Circles: orange = in national top 10 but missing from top 10 of individual location, blue = outside national top 10
四川 Sichuan - 9/10 surnames
Missing: 赵 Zhào
Added: 罗 Luó
贵州 Guizhou - 9/10 surnames
Missing: 赵 Zhào
Added: 罗 Luó
Fewest in common
浙江 Zhejiang - 6/10 surnames
Missing: 杨 Yáng, 赵 Zhào, 黄 Huáng & 周 Zhōu
Added: 林 Lín, 叶 Yè, 郑 Zhèng & 徐 Xú
广西 Guangxi - 6/10 surnames
Missing list: 赵 Zhào, 黄 Huáng, 周 Zhōu & 吴 Wú
Added: 梁 Liáng, 韦 Wéi, 陆 Lù & 卢 Lú
上海 Shanghai - 6/10 surnames
Missing: 刘 Liú, 杨 Yáng, 赵 Zhào & 黄 Huáng
Added: 朱 Zhū, 徐 Xú, 沈 Shěn & 陆 Lù
2) Find the average national rank for each location’s top 10 list.
OK, this section is a little confusing. Basically, I was thinking that just counting overlapping surnames this isn’t necessarily the best metric. When it comes to surnames outside the national top 10, just counting like I did above can’t distinguish a rank of 100 from a rank of 11! So I decided to take averages.
For each location, I found the national rank of the its top 10 surnames and averaged them. This should give me an idea of which location’s top 10 surnames collectively rank the highest in the whole country. If you add up 1-10 and divide by 10, you get an average of 5.5, so that would be the minimum possible average.
Lowest average
Highlighting: red = lower than national rank, green = higher than national rank, no highlighting = same as national rank
Blue circling = outside of national top 10
Annotated numbers = national rank
湖北 Hubei - 6.2 average
Outside national top 10: 胡 Hú & 徐 Xú
安徽 Anhui - 6.3 average
Outside national top 10: 徐 Xú & 孙 Sūn
江苏 Jiangsu - 6.5 average
Outside national top 10: 徐 Xú & 朱 Zhū
Highest average
海南 Hainan - 30.5 average
Outside national top 10: 符 Fú, 林 Lín & 郑 Zhèng
广西 Guangxi - 28.2 average
Outside national top 10: 梁 Liáng, 韦 Wéi, 陆 Lù & 卢 Lú
上海 Shanghai - 16.2 average
Outside national top 10: 朱 Zhū, 徐 Xú, 沈 Shěn & 陆 Lù
3) Add the difference in rank of surnames for each location to create a composite score.
Then I started to think about order within the top 10. After all, a province for which 李 Lǐ ranks #10 should be treated differently from one where 李 Lǐ is #1. So I wanted to capture the difference between a surname’s national rank and its rank for individual locations.
For example, in 广东 Guangdong, 陈 Chén ranks #1, but it’s #5 in the whole country. The difference is 5 - 1 = 4. I did this for the other 9 surnames in 广东 Guangdong’s top 10 as well and added the numbers to get a composite score. Then I repeated this for the other locations.
I used absolute values—otherwise a positive difference and negative difference would offset each other! But I used + and - signs in the images below to show more information. You could also divided by 10 to get the average difference for the top 10 surnames each location.
Lowest score
Highlighting: red = lower than national rank, green = higher than national rank, no highlighting = same as national rank
Blue circling = outside of national top 10
Annotated numbers = difference b/t national rank and local rank, with (+) indicating a higher rank locally and (-) indicating a lower rank locally
安徽 Anhui - 14 score
四川 Sichuan - 15 score
宁夏 Ningxia - 20 score
Highest score
海南 Hainan - 264 score
广西 Guangxi - 253 score
山西 Shanxi - 113 score
上海 Shanghai - 113 score
4) For each location, determine how many surnames have no difference in rank.
As an extension of the above, for each location, I counted the number of top 10 surnames that had no difference in rank compared to the national rank. Let’s look closer at locations whose top 10 lists had the fewest changes:
四川 Sichuan - 6 surnames
李 Lǐ, 刘 Liú, 陈 Chén, 杨 Yáng, 黄 Huáng & 吴 Wú
青海 Qinghai - 4 surnames
李 Lǐ, 刘 Liú, 杨 Yáng & 吴 Wú
云南 Yunnan - 4 surnames
李 Lǐ, 陈 Chén, 赵 Zhào & 周 Zhōu
重庆 Chongqing - 4 surnames
李 Lǐ, 刘 Liú, 杨 Yáng & 黄 Huáng
I’ll also list the locations for which no surnames had the same rank as in the top 10:
广东 Guangdong
福建 Fujian
江西 Jiangxi
江苏 Jiangsu
贵州 Guizhou
Summary: Which location’s top 10 is closest to the national top 10?
We just saw several different ways of looking at this. Someone who is better at math than I am would probably devise a way to combine the different metrics into a single score. I’m just going to recap which locations we saw appear the most.
Overall most similar: 四川 Sichuan & 安徽 Anhui
Overall least similar: 广西 Guangxi, 上海 Shanghai & 海南 Hainan
5) Find which surnames appear on the most and least location top 10 lists.
This wasn’t something I was initially curious about, but after my analyses above, I grew curious. I’m just looking at the national top 10 surnames here. There are 30 locations total, so 30 is the highest possible number.
So 李 Lǐ and 张 Zhāng are the only two that appear in the top 10 for all 30 locations!
I also thought it would be interesting to see which locations are missing for the surnames that were close to 30/30:
陈 Chén - 29/30
Missing: 新疆 Xinjiang
王 Wáng - 28/30
Missing: 广东 Guangdong & 广西 Guangxi
刘 Liú - 28/30
Missing: 海南 Hainan & 上海 Shanghai
杨 Yáng - 26/30
Missing: 海南 Hainan, 浙江 Zhejiang, 江西 Jiangxi & 上海 Shanghai
6) Which surname ranks 1st in the most locations?
This questions grew pretty naturally off of the question above. I spent far too long making this map to go along with the numbers!
The winner is...王 Wáng with 15 locations! It’s so interesting to see how 王 Wáng is dominant in the north, 陈 Chén rules the southern coast, etc.
王 Wáng - 1st in 15 locations
李 Lǐ - 1st in 6 locations
陈 Chén - 1st in 4 locations
张 Zhāng - 1st in 3 locations
刘 Liú - 1st in 1 location
黄 Huáng - 1st in 1 location
The winner is...王 Wáng with 15 locations!
7) Mainland China vs. Taiwan vs. Hong Kong
I thought this would be an interesting comparison. I wanted to include Macau as well, but I had difficulty finding a list. Here is the Hong Kong data source.
Arrows: green = higher than Mainland rank, red = lower than Mainland rank
Circles: orange = in Mainland top 10 but missing from the HK and/or TW top 10, blue = outside Mainland top 10
I was actually surprised how similar the top 10s are for Taiwan and Hong Kong!
I also wanted to compare Taiwan and Hong Kong to Fujian and Guangdong, respectively. These are the two provinces in Mainland China that they are closest to.
Now, I’m no history expert, but I know that a lot of Taiwanese have roots in Fujian, so it makes a lot of sense that their top 10 lists look so similar. I don’t know much about the history of migration to Hong Kong, but as such a major economic center, I’m guessing people from all over China came to Hong Kong.
Thank you!
If you actually read this whole post, I’m impressed. Thank you!
I initially began working on this post in September 2021. Needless to say, this post ended up being a lot longer and taking up a lot more of my time than I had anticipated. I asked my dad to read over an earlier draft of this post for me, and he literally asked me, “why are you doing this?” I didn’t really have a concrete answer. I just thought it would be interesting to explore surnames a bit. And so here we are :)
Extended list - 大陆25大姓氏
李 Lǐ
王 Wáng
张 Zhāng
刘 Liú
陈 Chén
杨 Yáng
赵 Zhào
黄 Huáng
周 Zhōu
吴 Wú
徐 Xú
孙 Sūn
胡 Hú
朱 Zhū
高 Gāo
林 Lín
何 Hé
郭 Guō
马 Mǎ
罗 Luó
梁 Liáng
宋 Sòng
郑 Zhèng
谢 Xiè
韩 Hán
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