Yoshinobu Oikawa - Japanese historian and economist. Article "老川慶喜" in Japanese Wikipedia has 23.9 points for quality (as of August 1, 2024). The article contains 17 references and 12 sections. The article also contains quality flaw template, which reduce quality score.
Since the creation of article "老川慶喜", its content was written by 18 registered users of Japanese Wikipedia and edited by 18 registered Wikipedia users in all languages.
The article is cited 54 times in Japanese Wikipedia and cited 54 times in all languages.
The highest Authors Interest rank from 2001:
The highest popularity rank from 2008:
There is 1 language version for this article in the WikiRank database (of the considered 55 Wikipedia language editions).
The quality and popularity assessment was based on Wikipédia dumps from August 1, 2024 (including revision history and pageviews for previous years).
| Best Rank | Japanese: #271181 10.2022 |
Global: #2045901 09.2010 |
| Best Rank | Japanese: #9262 09.2013 |
Global: #367291 09.2010 |
On 19 November 2024 in multilingual Wikipedia, Internet users most often read articles on the following topics: Mike Tyson, Elon Musk, MGM-140 ATACMS, Gladiator II, International Men's Day, 2024–25 UEFA Nations League, Donald Trump, Jake Paul, Jon Jones, deaths in 2024.
In Japanese Wikipedia the most popular articles on that day were: 高畑充希, 岡田将生, 谷川俊太郎, こっちのけんと, 立花孝志, 鈴木唯, 斎藤元彦, 片岡京子, 奥谷謙一, 鈴木紗理奈.
The WikiRank project is intended for automatic relative evaluation of the articles in the various language versions of Wikipedia. At the moment the service allows to compare over 44 million Wikipedia articles in 55 languages. Quality scores of articles are based on Wikipedia dumps from August, 2024. When calculating current popularity and AI of articles data from July 2024 was taken into account. For historical values of popularity and AI WikiRank used data from 2001 to 2023... More information
$$Quality=1/c∑↙{i=1}↖c nm_i-RS=1/5(8.86+6.34+0+38.71+71.7)-1.26$$
where:
The results of the calculations may be slightly different because of rounded values of normalized metrics in formulas
