Izdvojeno s Wikimedije za septembar 2015.
"Operating_a_Computer_Keyboard_MOD_45158106.jpg" from the UK Ministry of Defence, freely licensed under OGL 1.0.; "Funds Dissemination Committee November 2015 at Wikimedia Foundation Office.jpg" by MGuss (WMF), freely licensed under CC0 1.0; "Revscore_WP.jpg" by Mun May Tee, freely licensed under CC BY-SA 4.0.; Collage by Andrew Sherman.
The WMF, in collaboration with several volunteers and researchers, has released a new artificial intelligence service designed to improve the way editors maintain the quality of Wikipedia. This service empowers Wikipedia editors by helping them discover damaging edits and can be used to immediately “score” the quality of any Wikipedia article. We’ve made this artificial intelligence available as an open web service that anyone can use.
Wikipedia is edited about half a million times per day. In order to maintain the quality of Wikipedia, this firehose of new content needs to be constantly reviewed by Wikipedians. The Objective Revision Evaluation Service (ORES) functions like a pair of X-ray specs, the toy hyped in novelty shops and the back of comic books—but these specs actually work to highlight potentially damaging edits for editors. This allows editors to triage them from the torrent of new edits and review them with increased scrutiny.
By combining open data and open source machine learning algorithms, our goal is to make quality control in Wikipedia more transparent, auditable, and easy to experiment with.
Our hope is that ORES will enable critical advancements in how we do quality control—changes that will both make quality control work more efficient and make Wikipedia a more welcoming place for new editors. ORES brings automated edit and article quality classification to everyone via a set of open Application Programming Interfaces (APIs). The system works by training models against edit- and article-quality assessments made by Wikipedians and generating automated scores for every single edit and article.
We’ve been testing the service for a few months and more than a dozen editing tools and services are already using it. We’re beating the state of the art in the accuracy of our predictions. The service is online right now and it is ready for your experimentation.
This week, the Funds Dissemination Committee (FDC) recommended the distribution of almost $3.8 million to 11 independent affiliate organisations around the world. This committee is composed of nine elected and appointed volunteers from countries around the world; each has been editing Wikimedia projects for over a decade, and this round of recommendations is the first of two they will conduct in the Annual Plan Grant (APG) 2015–16 process.
Applicant Amount requested Amount recommended Indicative recommendation
in USD (approx)
Change in allocation
from last year
Amical Wikimedia (Catalan language) EUR 68,000 EUR 68,000 $76,000 100.0% −17.2% Wikimedia Argentina* USD 241,680 USD 232,500 $232,500 96.2% 9.7% Wikimedia CH (Switzerland) CHF 315,000 CHF 294,000 $305,000 93.3% −16.0% Wikimedia Deutschland e.V. (Germany) EUR 1,500,000 EUR 1,200,000 $1,346,000 80.0% 42.9% Wikimedia Israel* NIS 834,000 NIS 834,000 $212,000 100.0% 8.3% Wikimedia Nederland (Netherlands) EUR 340,000 EUR 340,000 $381,000 100.0% 11.8% Wikimedia Serbia EUR 112,500 EUR 112,500 $126,000 100.0% 13.9% Wikimedia Sverige (Sweden) SEK 2,616,000 SEK 2,616,000 $309,000 100.0% 2.3% Wikimedia UK* (United Kingdom) GBP 310,000 GBP 277,300 $427,000 89.5% −11.7% Wikimedia Ukraine USD 75,000 USD 75,000 $75,000 100.0% 82.2% Wikimedia Österreich (Austria) EUR 250,000 EUR 250,000 $280,000 100.0% 9.6% Total ~ USD 4,189,000 ~$3,770,000
Google Summer of Code and Outreachy are two software development internship programs that Wikimedia participates in every year. For the last nine years, college students have applied to be a part of the coding summer, one of many outreach programs operated by the Wikimedia Foundation.
For the first time, all Wikimedia projects that passed the evaluation were immediately deployed in production or Wikimedia Labs. Here they are:
- TranslateWiki is a popular translation platform used by many projects across Wikimedia and several times as many outside it. Originally developed single-handedly by Niklas Laxström, the platform has expanded significantly since its launch in 2006. This project aims to add a Search feature to the Translate extension.
- Crosswatch is a cross-wiki watchlist for all Wikimedia wikis. The goal of the project is to help editors who are active in several wikis to monitor changes and generally to provide a better watchlist for all editors.
- Wikivoyage has a special preference for showing page wide banners at the top of each of their articles to enhance their aesthetic appeal. An example of such a banner can be seen here. The project is all about addressing these issues and adding capabilities through a Mediawiki extension to take the banner experience to the next level.
- LanguageTool is an extension for VisualEditor that enables language proofing support in about twenty languages.
- Newsletter Extension for MediaWiki offers a catalog with all the newsletters available in a wiki farm, and the possibility to subscribe/unsubscribe and receive notifications without having to visit or be an active editor of any wiki.
- ve-graph is a module within the Graph extension that aims to bring graph editing tools to VisualEditor in order to bridge the gap between editors and Vega, the visualization engine powering graphs in MediaWiki pages.
- Ukrainian Wikipedia reaches 600,000 articles: With Окисно-відновні індикатори (Redox indicators), substances that are used in chemistry to determine the equivalence point of an redox reaction, the Ukrainian-language Wikipedia reached a milestone.
- Wikimedia Foundation begins annual year-end contribution campaign: On December 1, the WMF began its annual fundraiser drive on the English Wikipedia to support Wikipedia and the various sister projects. It aims to raise US$25 million.
- Wikipedia Town and Wikipedia ARTS in Kyoto: Wikipedia Town helps people find local information and edit Wikipedia articles—people who used to be merely readers of Wikipedia. Every one of them enjoys working in their own roles: people enjoy working on the same articles as a team, helping each other. Some may not be good at using computers, but other participants can jump in to help uploading their photographs taken during the trail to Wikimedia Commons.
Andrew Sherman, Digital Communications Intern, Wikimedia Foundation