Wikimedia Apps/Suggested edits/ja



提案された編集へようこそ
提案された編集とは Android で利用できるウィキペディアを編集する新しい方法です. 是非試してみてください.



提案された編集とは
提案された編集を行い小さいながらも重要な情報を追加することで、ウィキペディアの記事に貢献する事ができます. ウィキペディアは誰もが簡単に記事を編集して貢献することができるという認識を高めることが目的です.



提案された編集を使ってみる
提案された編集のホームページは2つの項目によって構成されています. 1.プロフィール統計 2.投稿履歴および投稿の機会 貢献の機会は提案された編集の中心を担う項目です. ここでは、ウィキペディアに貢献する方法が確認できます. 現時点では、記事の説明および画像のキャプションを追加または翻訳するタスクを提供しています. プロのように編集する方法をもっと知りたいあるいは学びたいならば、下記の専用の節を確認してください：



さらなる投稿タイプが追加される予定ですので、しばらくお待ちください.

プロフィール統計は、提案された編集内のアクティビティに関する情報を表示します. 最初に提案された編集を行った時点から、内容が反映され始めます.

Tapping the card leads to the contribution history page. The contribution history lists all contribution types that are available in Suggested edits. You can filter by edit type and see how many pageviews the articles you’ve contributed to had in the past 30 days. Tapping an item in the contribution history list leads you the edit detail (diff) page, where even more infos about the particular edit are featured.
 * Contributions - 過去30日間の提案された編集による投稿数を表示します.
 * Pageviews - 提案された編集を使用して投稿した内容を他のユーザーが閲覧した回数の過去30日間の合計を表示します.
 * Edit streak - 提案された編集を利用して投稿した連続日数を表示します. しばらく投稿がない場合は、最後の投稿日が表示されます.
 * Edit quality - 投稿した内容が別の編集者により投稿前の状態に戻された/取り消された回数を表示します. 元に戻された投稿が少なければ、それはあなたの編集品質が高いという事です.



記事の説明




記事の説明とは

 * 短く、多言語な項目の説明（例えば、ウィキペディアの記事）
 * 記事の説明は完全な文章ではなく、小さな情報の細片です.
 * 多くの場合、適切な長さは2から12単語の間です.



記事の説明は何に使われる？
記事の説明はアプリで読者が探している記事を識別するのを助けるためにウィキペディアの記事タイトルの下に表示されます. 記事の説明はウィキデータで保管および保守されており、同一または類似のラベルを持つ項目を区別することを意図しています. 説明はウィキペディアのサイトやアプリの外でも表示されます：例えば、Google検索.

記事の説明とは、読者が一目で主題を理解できるよう記事を要約したものです. ウィキデータ内の「説明」が使用されています.



記事の説明を記述するときの心得
説明は通常、短く1文で、体言止めで記述します. 例:


 * 「レオナルド・ダ・ヴィンチによる絵画」（「モナ・リザ」の説明）
 * 「ネパールとチベットの間に位置するヒマラヤ山脈にある、世界最高峰の山」（「エベレスト」の説明）

良い記事の説明を書くためのその他のヒント：


 * 将来的に変更される可能性のある情報を避ける (例: 現在の首相の...)
 * 個人的主張、偏向あるいは宣伝が混じる言いまわしを避ける （例:最も有名な…）
 * 論争が起こるような主張を避ける

さらなる情報は説明についてのウィキデータのヘルプページで入手可能です.



記事の説明の詳細
記事の説明はウィキデータに格納され管理されます. ウィキデータはウィキメディア財団によるオープンな共同編集データベースです. ウィキペディアをはじめとする様々なウィキメディアプロジェクトで利用されています.


 * ウィキデータについて
 * ウィキデータの「記事の説明」についてのヘルプページ
 * 英語で記事の説明を記述する場合のガイドライン
 * Guidelines for writing short descriptions on English Wikipedia



画像のキャプション


画像のキャプションとは

 * 短く、多言語な画像ファイルの説明
 * 長さは250文字以内に制限され、マークアップを含めることはできません



画像のキャプションは何に使われる？
画像のキャプションは画像を説明して読者が画像の意味や文脈を理解するのを助けます. They are also used to provide alternative information for an image if people cannot view it, e.g. because of a slow internet connection or if people use a screen reader.

Tips for creating image captions
Image captions should be short descriptions of what the image shows. They are generally four to twelve words long. They can also contain information about the artist or creator of the image.

Remember to keep your captions neutral. Avoid making value judgements such as "beautiful", "good" or "ugly".

The difference between the image description and the image caption is that the description can have a lot of information about the file. If the image is a scan it can have details about the original photograph or artwork. It can have links. The image caption should ignore all this and just describe what it shows, not the information about the file.

いい画像のキャプションの例:


 * Aim to briefly describe the contents of an image
 * Describe the image such that those with vision or other impairments can imagine what it looks like
 * Contain some keywords that people are likely to use to search for an image (so a picture of a cat should include the word "cat" somewhere in the caption)

In many cases, the caption will be similar or identical to the description (or even the file title!). For example, File:Fire station Hallstatt - October 2017 - 02.jpg has the English caption "Hallstatt fire station in October 2017".

More on image captions
Image captions are stored and maintained on Wikimedia Commons, a project of the Wikimedia Foundation which provides an online repository of free-use images, sounds, other media, and JSON files supporting Wikipedia and other projects.

Image tags

 * 関連項目: Commons:Depicts 

What are image tags?
"Image tags" is a shorthand phrase for what the Commons community calls Depicts statements.

What are image tags used for?
By adding image tags, you will help make images easier to search for on Commons, the free license image repository that Wikipedia uses for images in its articles.



イメージタグを追加する際の心得
Please add tags conservatively. If there are multiple items clearly and deliberately depicted by the image, all should be added as separate tags, within reason. For example, should be tagged with "Bonnie and Clyde", "Bonnie Parker", and "Clyde Barrow". Identify the most important thing(s) in the image.

Be as specific as you can. Search for the most relevant tags by tapping "+ Add tag". You may see a number of search results, but resist the urge to add a large number of semi-relevant tags. In the example shown below, the picture is of the Williamsburg Bridge, but that tag has not been added. Add it yourself by tapping "+ Add tag" and searching for "Williamsburg Bridge".

Where is the train algorithm task?
Due to the train image algorithm task serving as a MVP for the Android team to learn from and build the full image matching feature, the train image algorithm MVP has been sunset. With the help of Wikipedians like you, we have improved the algorithm and have enough learnings to proceed with the next phase of our work to build the full image matching feature. Our lessons from the experiment will be available on the Add an Image project page. You can watch participate in the next phase of this work at Growth team's project page.

What is the train algorithm task?
Wikipedia articles are written and edited by thousands of volunteers from around the world. Unfortunately, many articles lack images. The Train Algorithm task is a type of Suggested Edits task that will show logged-in Android users articles and images along with its associated information, so that users can determine if the image is a good illustration of the contents of the article displayed.

The images will be suggested to you using an algorithm. The algorithm will pull images from other sources and suggest a match with an article that does not have an image.

Unlike other Suggested Edits tasks, the Train Algorithm task will not save any edits to any Wiki projects and is a temporary task. The purpose of the Train Algorithm task is to gather data, improve our image matching algorithm, and inform our design for future releases of an image matching task on Android and Mobile Web.



Tips for training the image algorithm
To best determine if an image is the right match for an article you should review:
 * Image (zoom in to review more details)
 * Image file name
 * Image description
 * Image suggestion reason
 * Article content

Dialog options
In the task you can select Yes, No or Not Sure, to the question of if you would add the image to an article.


 * Yes indicates the image is a good illustration to help readers understand the topic of the Wikipedia article
 * No indicates the image would not help readers understand the topic of the Wikipedia article. Reasons the image would not help be a good fit for the article includes:
 * Not Relevant, which indicates the image depicts a topic that is not associated with the article that is being shown
 * Not enough Information, which indicates the metadata shared with the image does not provide enough details for you to confidently indicate the image would help readers better understand the article
 * Offensive, which indicates the image is inappropriate
 * Low Quality, which indicates you can not see the image well enough to confidently say it is a good illustration of the article being shown and help readers understand the topic
 * Don't know this subject, which indicates you do not feel you have the needed expertise to determine if the image would help readers understand the topic of the Wikipedia article
 * Cannot read the language, which indicates the words written in the image, or the metadata is in a language you do not understand, and are then unable to confidently say the image would help readers understand the topic of the Wikipedia article
 * Other, which indicates there is another reason the image is not a good match for the article
 * Not Sure indicates you are not certain whether or not the image would help readers understand the topic of the Wikipedia article. Reasons for not being sure could include:
 * Not enough Information, which indicates the metadata shared with the image does not provide enough details for you to confidently indicate the image would help readers better understand the article
 * Cannot see image, which indicates you can not see the image well enough to confidently say it is a good illustration of the article being shown and help readers understand the topic
 * Don't know this subject, which indicates you do not feel you have the needed expertise to determine if the image would help readers understand the topic of the Wikipedia article
 * Don't understand the task, which indicates you do not understand the Train Image Algorithm task
 * Cannot read the language, which indicates the words written in the image, or the metadata is in a language you do not understand, and are then unable to confidently say the image would help readers understand the topic of the Wikipedia article
 * Other, which indicates there is another reason you are not sure if the image would be a good match for the article

Daily goal
The 'Train image algorithm' task consists of a daily goal. To reach the daily goal, you need to evaluate 10 image suggestions. The goal resets to 0 on the next day. You can always evaluate more than 10 image suggestions per day, this should just serve as an indicator of your daily progress.

When will you roll out the full image matching task?
The Android and Growth teams will collaboratively evaluate the outcomes of this MVP, and determine next steps for a task that places images in articles in July 2021. During that time, the teams will update the MVP project page with our findings and share when we will offer the image matching task.

How can I follow the outcome of this MVP when it ends?
You can watch our Add an Image MVP project page for updates about this effort. We would also value your feedback on our talk page.