Dear all,
I wish to invite you to episode 16 of WikiAfrica Hour, titled "*Wiki
Identity*."
The episode is focused on sharing details about how the various Wiki
projects are identified, as regards logo/brand designs/modifications.
The following guests have been invited to make the episode very engaging:
1. Erina Mukuta - Wikimedia Sound logo community liaison
2. Zack McCune - Director of Brand, Wikimedia Foundation
3. Amir Sadabani - Member, Code of Conduct committee/MediaWiki volunteer
Date: 23rd September, 2022
Time: 4pm UTC
Details: w.wiki/5dft <https://t.co/c9ZueUzjTW>
Regards,
Ceslause Ogbonnaya
*WikiAfrica Hour host*
Fellow Wikimedians,
We are delighted to announce the formation of the Core Organizing Team
(COT) for Wikimania 2023.
We are also calling for volunteers to be a member of its sub-committees
such as Scholarship, Programming, Trust&Safety, Multimedia/virtual
environment.
Go to the meta wiki link below for details.
https://meta.wikimedia.org/wiki/Wikimania_2023
Note: Please be explicit on the committee you prefer to join. We will first
reach out to those who had indicated a committee they like to join.
Do ensure that your user account preferences on meta wiki have your email
address enabled so that organizers would reach back to you for the result
of your submitted expression of interest.
Thank you.
Kind regards,
Butch Bustria
On behalf of the Wikimania 2023 COT
Hi everyone,
The Wikimedia sound logo contest has officially launched
<https://diff.wikimedia.org/2022/09/13/the-call-is-out-wikimedia-sound-logo-…>,
welcoming scores of submissions from around the world. From 13 September to
10 October, everyone, everywhere is invited to play a part in The Sound of
All Human Knowledge <https://soundlogo.wikimedia.org/>.
Please revisit your on-wiki space
<https://meta.wikimedia.org/wiki/Communications/Sound_Logo> for a video
introduction to audio production, a collection of sounds to get you
inspired, the campaign toolkit to help vibrate the call through your
networks, free and open source tools, and frequently asked questions. And
earmark this, on 29 September, we will be having a drop-in clinic to help
your submissions along.
Many community members have provided us with valuable input and guidance
along the journey of this project – from an initial concept exploration to
the recent contest proposal. Thank you. With so much expertise in
international contests and visual logo searches in the movement, we are all
curious to learn how sound and audio production will play out in harmony
with our values, scaling up our projects for accessibility and audio
devices. Read more about it on Diff
<https://diff.wikimedia.org/2022/03/15/you-cant-see-the-puzzle-globe-on-an-a…>.
Looking forward to hearing from you.
On behalf of the sound logo project team,
Mehrdad
*Mehrdad Pourzaki*
Lead Movement Communications Specialist
wikimediafoundation.org
Greetings,
The WikiCredibility Grants Initiative (WikiCred) is pleased to announce
that it is now accepting applications from individuals, groups, or
organizations seeking funding support for the development of tools,
projects or initiatives that strengthen credibility and reliability of
information within Wikimedia projects.
More information along with a full CFP and a link to the application portal
can be found on Meta <https://meta.wikimedia.org/wiki/WikiCred>.
Applications will be accepted until October 21, 2022.
Wikimedia DC is assisting with the administration of this grant program.
Best regards,
Ariel Cetrone
--
Ariel Cetrone
Pronouns: She/Her/Hers
Institutional Partnerships Manager
Wikimedia District of Columbia
215-828-4517
ariel.cetrone(a)wikimediadc.org <kirill.lokshin(a)wikimediadc.org>
wikimediadc.org
Wikipedia Username: Ariel Cetrone (WMDC)
<https://en.wikipedia.org/wiki/User:Ariel_Cetrone_(WMDC)>
LinkedIn: Ariel Cetrone
<https://www.linkedin.com/in/ariel-cetrone-604025a8/>
Hi everyone,
A while back, Wikimedia Sverige announced the upcoming Helpdesk of the
Content Partnerships Hub, a service to support Wikimedians that want to
work with partners to share content on the Wikimedia platforms.
Now, when the Helpdesk is finally up and running, the next thing needed are
your requests for help!
I just published a blog post on Diff
<https://diff.wikimedia.org/2022/09/02/do-you-want-support-with-content-part…>
[1]
where I try to outline the process. A lot is still under development, but
you can already now read more on the Helpdesk portal on Meta
<https://meta.wikimedia.org/wiki/Content_Partnerships_Hub/Helpdesk> [2].
The basic idea, however, is that anyone that wants support with a project
sends an email to *helpdesk(a)wikimedia.se <helpdesk(a)wikimedia.se>*. Describe
what you want to do and what you want to achieve, and in what way it
contributes to filling a gap on the platforms, and the Helpdesk will do
what it can to help. The support can be anything from technical and batch
uploads to policy-making or advocacy. We look forward to getting your
proposals!
We also want to invite Wikimedians to join the work. We plan to establish
working groups to support with anything from communication to advocacy and
actual uploads. If you are a volunteer or an affiliate that wants to
support, please reach out as well! You can email *helpdesk(a)wikimedia.se
<helpdesk(a)wikimedia.se> *for this as well.
Best
*Eric Luth*
Projektledare engagemang och påverkan | Project Manager, Involvement and
Advocacy
Wikimedia Sverige
eric.luth(a)wikimedia.se
+46 (0) 765 55 50 95
Stöd fri kunskap, bli medlem i Wikimedia Sverige.
Läs mer på blimedlem.wikimedia.se
[1]
https://diff.wikimedia.org/2022/09/02/do-you-want-support-with-content-part…
[2] https://meta.wikimedia.org/wiki/Content_Partnerships_Hub/Helpdesk
This paper (first reference) is the result of a class project I was part of
almost two years ago for CSCI 5417 Information Retrieval Systems. It builds
on a class project I did in CSCI 5832 Natural Language Processing and which
I presented at Wikimania '07. The project was very late as we didn't send
the final paper in until the day before new years. This technical report was
never really announced that I recall so I thought it would be interesting to
look briefly at the results. The goal of this paper was to break articles
down into surface features and latent features and then use those to study
the rating system being used, predict article quality and rank results in a
search engine. We used the [[random forests]] classifier which allowed us to
analyze the contribution of each feature to performance by looking directly
at the weights that were assigned. While the surface analysis was performed
on the whole english wikipedia, the latent analysis was performed on the
simple english wikipedia (it is more expensive to compute). = Surface
features = * Readability measures are the single best predictor of quality
that I have found, as defined by the Wikipedia Editorial Team (WET). The
[[Automated Readability Index]], [[Gunning Fog Index]] and [[Flesch-Kincaid
Grade Level]] were the strongest predictors, followed by length of article
html, number of paragraphs, [[Flesh Reading Ease]], [[Smog Grading]], number
of internal links, [[Laesbarhedsindex Readability Formula]], number of words
and number of references. Weakly predictive were number of to be's, number
of sentences, [[Coleman-Liau Index]], number of templates, PageRank, number
of external links, number of relative links. Not predictive (overall - see
the end of section 2 for the per-rating score breakdown): Number of h2 or
h3's, number of conjunctions, number of images*, average word length, number
of h4's, number of prepositions, number of pronouns, number of interlanguage
links, average syllables per word, number of nominalizations, article age
(based on page id), proportion of questions, average sentence length. :*
Number of images was actually by far the single strongest predictor of any
class, but only for Featured articles. Because it was so good at picking out
featured articles and somewhat good at picking out A and G articles the
classifier was confused in so many cases that the overall contribution of
this feature to classification performance is zero. :* Number of external
links is strongly predictive of Featured articles. :* The B class is highly
distinctive. It has a strong "signature," with high predictive value
assigned to many features. The Featured class is also very distinctive. F, B
and S (Stop/Stub) contain the most information.
:* A is the least distinct class, not being very different from F or G. =
Latent features = The algorithm used for latent analysis, which is an
analysis of the occurence of words in every document with respect to the
link structure of the encyclopedia ("concepts"), is [[Latent Dirichlet
Allocation]]. This part of the analysis was done by CS PhD student Praful
Mangalath. An example of what can be done with the result of this analysis
is that you provide a word (a search query) such as "hippie". You can then
look at the weight of every article for the word hippie. You can pick the
article with the largest weight, and then look at its link network. You can
pick out the articles that this article links to and/or which link to this
article that are also weighted strongly for the word hippie, while also
contributing maximally to this articles "hippieness". We tried this query in
our system (LDA), Google (site:en.wikipedia.org hippie), and the Simple
English Wikipedia's Lucene search engine. The breakdown of articles occuring
in the top ten search results for this word for those engines is: * LDA
only: [[Acid rock]], [[Aldeburgh Festival]], [[Anne Murray]], [[Carl
Radle]], [[Harry Nilsson]], [[Jack Kerouac]], [[Phil Spector]], [[Plastic
Ono Band]], [[Rock and Roll]], [[Salvador Allende]], [[Smothers brothers]],
[[Stanley Kubrick]]. * Google only: [[Glam Rock]], [[South Park]]. * Simple
only: [[African Americans]], [[Charles Manson]], [[Counterculture]], [[Drug
use]], [[Flower Power]], [[Nuclear weapons]], [[Phish]], [[Sexual
liberation]], [[Summer of Love]] * LDA & Google & Simple: [[Hippie]],
[[Human Be-in]], [[Students for a democratic society]], [[Woodstock
festival]] * LDA & Google: [[Psychedelic Pop]] * Google & Simple: [[Lysergic
acid diethylamide]], [[Summer of Love]] ( See the paper for the articles
produced for the keywords philosophy and economics ) = Discussion /
Conclusion = * The results of the latent analysis are totally up to your
perception. But what is interesting is that the LDA features predict the WET
ratings of quality just as well as the surface level features. Both feature
sets (surface and latent) both pull out all almost of the information that
the rating system bears. * The rating system devised by the WET is not
distinctive. You can best tell the difference between, grouped together,
Featured, A and Good articles vs B articles. Featured, A and Good articles
are also quite distinctive (Figure 1). Note that in this study we didn't
look at Start's and Stubs, but in earlier paper we did. :* This is
interesting when compared to this recent entry on the YouTube blog. "Five
Stars Dominate Ratings"
http://youtube-global.blogspot.com/2009/09/five-stars-dominate-ratings.html…
I think a sane, well researched (with actual subjects) rating system
is
well within the purview of the Usability Initiative. Helping people find and
create good content is what Wikipedia is all about. Having a solid rating
system allows you to reorganized the user interface, the Wikipedia
namespace, and the main namespace around good content and bad content as
needed. If you don't have a solid, information bearing rating system you
don't know what good content really is (really bad content is easy to spot).
:* My Wikimania talk was all about gathering data from people about articles
and using that to train machines to automatically pick out good content. You
ask people questions along dimensions that make sense to people, and give
the machine access to other surface features (such as a statistical measure
of readability, or length) and latent features (such as can be derived from
document word occurence and encyclopedia link structure). I referenced page
262 of Zen and the Art of Motorcycle Maintenance to give an example of the
kind of qualitative features I would ask people. It really depends on what
features end up bearing information, to be tested in "the lab". Each word is
an example dimension of quality: We have "*unity, vividness, authority,
economy, sensitivity, clarity, emphasis, flow, suspense, brilliance,
precision, proportion, depth and so on.*" You then use surface and latent
features to predict these values for all articles. You can also say, when a
person rates this article as high on the x scale, they also mean that it has
has this much of these surface and these latent features.
= References =
- DeHoust, C., Mangalath, P., Mingus., B. (2008). *Improving search in
Wikipedia through quality and concept discovery*. Technical Report.
PDF<http://grey.colorado.edu/mediawiki/sites/mingus/images/6/68/DeHoustMangalat…>
- Rassbach, L., Mingus., B, Blackford, T. (2007). *Exploring the
feasibility of automatically rating online article quality*. Technical
Report. PDF<http://grey.colorado.edu/mediawiki/sites/mingus/images/d/d3/RassbachPincock…>
The Wikimedia Deutschland Movement Strategy & Global Relations Team has
published Paper #2 in the WMDE-authored series on Movement Strategy topics.
Titled Decentralized Fundraising, Centralized Distribution
<https://meta.wikimedia.org/wiki/Wikimedia_Deutschland/Decentralized_Fundrai…>,
this research report describes the fundraising and distribution practices
of eight large international NGO confederations and networks, and puts them
in the context of the changing Wikimedia Movement.
2030 Movement Strategy calls on us to change many things - among them how
we generate and share funds among regions, affiliates and communities.
Subsidiarity, equity, and participation are just some of the key values and
principles to be incorporated.
At the 2022 Wikimedia Summit, funding and redistribution will be discussed
in detail. Wikimedians will later write their agreements in the charter,
hub policies, and the fundraising policy.
The report's overview of existing practices will help create a joint
language and inform the discussions.
The paper deliberately refrains from recommendations. In addition to the
research, it does provide an overview of the history of Wikimedia resource
development, discusses the elements of movement strategy related to
funding, and finally poses a series of questions helpful to frame the
further conversation.
Looking forward to comments and a good discussion.
best,
Nikki
Nikki Zeuner
Senior Advisor Global Partnerships
Wikimedia Deutschland e. V. | Tempelhofer Ufer 23-24 | 10963 Berlin
Tel. (030) 219 158 26-32
Mobile (0151) 50824711
https://wikimedia.de
Unsere Vision ist eine Welt, in der alle Menschen am Wissen der Menschheit
teilhaben, es nutzen und mehren können. Helfen Sie uns dabei!
https://spenden.wikimedia.de
Bleiben Sie auf dem neuesten Stand! Aktuelle Nachrichten und spannende
Geschichten rund um Wikimedia, Wikipedia und Freies Wissen im Newsletter: Zur
Anmeldung <https://www.wikimedia.de/newsletter/>.
Wikimedia Deutschland — Gesellschaft zur Förderung Freien Wissens e. V.
Eingetragen im Vereinsregister des Amtsgerichts Berlin-Charlottenburg unter
der Nummer 23855 B. Als gemeinnützig anerkannt durch das Finanzamt für
Körperschaften I Berlin, Steuernummer 27/029/42207.
Hello!
Thanks to everyone who took the time to provide their valuable feedback on
the Toolhub taxonomy[0]!
After a round of community feedback and input, we made the following
decisions about which categories and values to implement in the first
productionized version of the taxonomy.
=== Summary of Changes ===
1.
Exclude the proposed Programming language attribute.
2.
Exclude the proposed Platform attribute
3.
Revise the Tasks attribute values:
1.
Remove "Creating or uploading content"
2.
Add "Creating new content"
3.
Rename "Generating and recommending content" to "Recommending content"
4.
Revise the Content types attribute values:
1.
Add additional level of hierarchy to group content types and enable
both broad or specific values to be applied.
2.
Remove "Files".
3.
Split "Maps" and "Geographic Data"
Find more details of additional changes on the decision record log page[0]
=== Next Steps ===
The team will continue to observe and improve the taxonomy as the community
continues to use Toolhub.
We will monitor tags and community created lists to determine if certain
attributes would be useful or feasible in the future.
[0]: https://meta.wikimedia.org/wiki/Toolhub/Data_model#Taxonomy_v2 [1]:
https://meta.wikimedia.org/wiki/Toolhub/Decision_record#Taxonomy_v2
--
Seyram Komla Sapaty
Developer Advocate
Wikimedia Cloud Services