Article “かまいたちのヘイ!タクシー!” in Japanese Wikipedia has 18.9 points for quality (as of July 1, 2025). The article contains 17 references and 9 sections. The article also contains templates indicating quality issues, therefore its score was reduced by 1 points.
Since the creation of article “かまいたちのヘイ!タクシー!”, its content was written by 24 registered users of Japanese Wikipedia and edited by 24 registered Wikipedia users in all languages.
The article is cited 80 times in Japanese Wikipedia and cited 80 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 July 1, 2025 (including revision history and pageviews for previous years).
| Best Rank | Japanese: #85335 02.2021 |
Global: #735014 09.2020 |
| Best Rank | Japanese: #6022 09.2018 |
Global: #285890 09.2018 |
On 12 August 2025 in multilingual Wikipedia, Internet users most often read articles on the following topics: Cristiano Ronaldo, Wednesday, Georgina Rodríguez, ChatGPT, Weapons, Jenna Ortega, deaths in 2025, Taylor Swift, Miguel Uribe Turbay, 2025–26 UEFA Champions League.
In Japanese Wikipedia the most popular articles on that day were: 日本航空123便墜落事故, 坂本九, 二階堂ふみ, カズレーザー, 北原遥子, やなせたかし, 柏木由紀子, 大竹七未, 大島花子, 舞坂ゆき子.
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 July, 2025. When calculating current popularity and AI of articles data from June 2025 was taken into account. For historical values of popularity and AI WikiRank used data from 2001 to 2025... More information
$$Quality=1/c∑↙{i=1}↖c nm_i-RS=1/5(11.64+6.14+0+29.03+52.8)-1$$
where:
The results of the calculations may be slightly different because of rounded values of normalized metrics in formulas
