Edit Review Improvements/ja

編集の査読改善 (ERI=Edit Review Improvements) とは共同開発チームによるプロジェクトで、現状の編集の査読段階がウィキの初学者に与えるかもしれない否定的な影響を軽減する方法を模索するものです. 編集の査読ならびに巡回のツールはほとんど、コンテンツの品質を保ち悪意のある人から守るという非常に重要な任務を果たすために設計されています. しかしながらある調査によると査読の段階で、特に自動化ツールや半自動化ツールを使用すると、誠実な編集の初学者を妨害するばかりか追い出してしまい、意図しない結果をもたらす可能性を示唆しています.

この問題の解決策を求める共同開発チームは、現状の編集と査読のワークフローから善意の初学者を隔てて、究極には精力的な貢献者に育つように励まして支える査読のプロセスを提供できないかと方法を探っています.

問題点

 * 調査によりウィキ編集の初学者は編集者として、とりわけ「編集の差し戻しを受けると編集量が減り、活動をやめてしまいがち」と示されています.
 * また同時に、自動化・半自動化査読ツールの導入が増えるに連れて、善意の初学者を排除する率が高まりました. これらのツールを使うと「初学者を拒絶することで、その望ましい定着率に与える負の影響が著しい」とされます.
 * 上記とは別に、編集の査読ツールは、破壊行為と戦う人ほか、ウィキの完全性と品質維持のために努力する人々には不可欠です. 初学者を助けて定着率を上げ、同時に破壊行為と戦うなど査読者の働きを助けるにはどうすればよいのでしょうか？

目標
このプロジェクトの最終的な目的とは編集者の定着率向上に影響を与えることであり、それは利用者コミュニティとの緊密な協議のもとに開発されたウィキメディア財団2016-17年年次計画の全体的な目標ともよく合っています.
 * 編集と査読によって、善意の初学者の心をくじくのではなく、より建設的な経験ができるようにします.
 * 最近の変更に関する豊富なデータを提供することで、さまざまなタイプの巡回者や編集者がより効率的に作業し、さまざまな関心を（例：破壊行為との戦い、初学者の支援） より効果的かつ目標を絞って追求できるようになります.

とりわけアプローチとしては、年間計画における製品チームの「新しいタイプのコンテンツ...キュレーションと共同開発ツールへの投資」という目標に追従しています.

解決策
この問題に取り組むには、査読者から苦戦している善意の初学者を見つけやすくすることが第一歩です. その実現には「最近の更新」の分析に、機械学習プログラムであるORES（Objective Revision Evaluation Service）を含むさまざまなソースから取り出したデータを使うよう提案します. 人間の判断で訓練された ORES の善意のモデルは、成功率98％の精度で誠実な編集の95％を検出できます. ORES により差し戻す編集とウィキを害する編集の予測もできます.

研究結果からも、特に初学者は編集を取り下げられると立ち直りにくいとわかっていますが、同時に、編集・査読、さらに場合によっては編集の取り下げさえも、newcomers. にとって強力な学習機会になる可能性も知られています. 初学者を支えて導くことに関心のある査読者から見てある一連の編集が、a) 却下される可能性は大きいけれど b) 誠意があると判断できる場合には、そのときこそ手助けするチャンスにしていただきたいのです.

以上の編集の分析を2通りの方法で利用者に提供します.


 * On the Special:Recent Changes page, where a suite of new filters will be provided as a beta feature (read a description of the planned new filters for edit review)
 * In a new machine-readable feed dubbed ReviewStream (ReviewStream Product Description), designed to be ingested by downstream edit-review tools.

現在の作業

 * To visualize possible product directions, the Collaboration Team is exploring design concepts while continuing to research the issues.
 * To better gauge the size of the problem and be able to track progress, we’re working to define and measure new-editor retention.


 * Design Research is organizing and conducting interviews with users touched by this issue in various ways, to better understand their motivations and workflows. Groups who will be interviewed in the near term include: anti-vandalism patrollers, recent changes patrollers, Teahouse hosts, Welcoming Committee members, and AfC reviewers.
 * The Research and Data team is working to make predictions better by refining the accuracy of prediction models.


 * There was a discussion of the project at Wikimania 2016, in June

「最近の更新」のしぼり込み機能を改善
詳細情報



In order to help reviewers to easily find the contributions they look for, we plan to improve the way filtering works on the Special:Recent Changes page. The goal is to make the list of contributions easy to filter, allow for more filter criteria (especially those relevant for helping newcomers) and facilitate combining multiple filters for different purposes.

This interactive prototype illustrates the filtering concept proposed. For additional context, you can check.

Before reaching there, this will be done in multiple steps inside a beta feature. More details below.

カスタムの手順
Initially, namespaces and tags won't be integrated into the filtering system. Filters related to ORES will be supported. These filters include:
 * Review. Filters that allow reviewers to focus on those contributions not reviewed yet, or those already processed by other reviewers.
 * Contribution quality. Filters that allow to identify contributions that are good or damaging.
 * User intent. Filters that allow to identify contributions that were made in good or bad faith.
 * User experience level. Filters that allow to target edits depending on the expertise of their author.

今後の計画
Creating the streams/pages of “teachable moments” described above has the potential to establish edit-review as a new space for instructing and supporting new editors. The mere existence of such a platform, however, won’t in itself ensure that this new practice will take root. To truly have an impact on newcomer retention, interventions may be required at multiple points in the editing and review cycles: before publication, to spot problems and enable authors to seek help; during review, to facilitate a constructive process; and even after review, to help new users overcome rejection and learn from from their experiences.

In addition to exploring ideas for intervening at various points, we’re pursuing answers to questions such as these:


 * How can we bring reviewers to this new activity?
 * What would make reviewers most effective in the job of supporting newcomers during edit review?
 * How can we make the process rewarding for reviewers, so that they stay involved?

The counter-vandalism community also has an important role to play in this arena. Richer data about edits and editors should make patrollers of all types not only more discriminating about which edits might be in good faith, but also more efficient at their job of combating harm. It will be important to work closely with vandalism fighters and others to understand how their processes and tools might best be adapted to realize these potential gains.

原則
As we pursue this project, the following principles will guide our planning.


 * Smart but human. Use technology to support rather than replace human interaction. Artificial intelligence can provide analysis, but humans should make decisions.
 * Cross-community. Find solutions that will work across language groups and projects, rather than building wiki-specific tools.
 * Platform not feature. Seek solutions that are extensible and reusable by current and future community-created and WMF tools.
 * Mobile. Although edit-review is not currently popular on mobile, consider mobile users carefully in our plans.
 * Adoption. In addition to creating new technology, focus on finding ways to encourage reviewers to adopt and continue to use the new tools.
 * Integration. In seeking new solutions, build on and integrate with existing practices whenever possible.
 * Incremental approach. As we move into this new area, proceed incrementally to each milestone and then evaluate where to go next.
 * Participatory design. Collaborate with editors and tool developers already working in this space.

関連文書

 * Grants:IdeaLab/Fast and slow new article review
 * Research:Newcomer survival models