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. これらのツールは「拒絶された初学者の定着率に及ぼす望ましくない効果を著しく増大させる」とみなされます.
 * 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
Ultimately this project aims to have an effect on editor retention, an objective that aligns well with the overall goals of the Wikimedia Foundation 2016-17 Annual Plan, developed in close consultation with the user community.
 * 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.
 * By providing richer data about recent changes, enable patrollers and edit-reviewers of all types to work more efficiently and to pursue diverse interests (e.g., fighting vandalism, supporting new users) in a more effective and targeted way.

The approach tracks in particular with the goals the Annual Plan lays out for the Product Team, which promise, among other things, to “Invest in new types of content…curation and collaboration tools.”

解決策
To begin to address the problems of struggling but good-faith newcomers, a good first step will be to ensure that reviewers can find them. To make this possible, we propose to analyze recent changes using data from a variety of sources, including and most notably the machine-learning program ORES (Objective Revision Evaluation Service). ORES’s good faith model, trained on human judgement, can find 95% of good-faith edits with 98% accuracy. ORES can also predict edits that will be reverted and those that are damaging to the wikis.

While research shows that new editors are particularly vulnerable to rejection, there’s also evidence that edit-review and even rejection can be a powerful learning experience for newcomers. For reviewers interested in supporting new users, then, a stream of edits that are a) likely to be reverted but which were b) made in good faith will, we hope, represent a string of teachable moments.

The edit analysis described above will be made available initially to users in two ways :


 * 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