Analysis of a Wikipedia article

For professor Gerhard Gries there is a Wikipedia article and as a consequence there was a Wikidata item. Actually there were two; they have been merged. Many articles have been attributed to professor Gries and a Scholia template was added to the article.

Professor Gries has his own lab: the "Gerhard and Regine Gries Lab". There is no article for his wife and, there was no red link. There was no Wikidata item for his wife but there were 59 links indicating her as a missing co-author. She has now 68 articles linked to her as an author or co-author. Regine Gries is now a "woman in red".

The article states in two places that he is a "Fellow of the Entomological Society of America" (2019). It is in the text and it is in a Wikipedia category. Relevant are two additional lists; the website of the Entomological Society of America and the result of a query at Wikidata (shown in a Scholia). All these lists are incomplete, the fellows of 2022 are not yet included on the SoA website; they can be found in a different place. The Scholia has been added to the category article on the English Wikipedia; in effect you will find several "fellows in red". 

Three additional awards are listed, awards that have no category on the article. One is for the "Fellow of the Entomological Society of Canada", its reference may be found here. Another one is for the Nan-Yao Su Award and finally the Gold Medal of the Entomological Society of Canada had to be added to Wikidata. There are always more "award winners in red" to be found or to be added.

Every list, every category in Wikipedia is likely to be incomplete. What we do not know may be relevant. With data on missing articles available in Wikidata, there are more more options to make Wikipedia more inclusive.

Thanks,

       GerardM

he, she, they/them

One ambition at Wikipedia is to have more articles about women.  The Women in Red project does really well, slowly but surely the balance between articles about males and females is improving. How do we know this: in Wikidata we have a database we can query and it shows over time.

Obviously, both many deserving men and women could get an article in future and particularly many scientists are already known in Wikidata through their publications. So how do we know the gender of these scientists? Because of a name like Emma or Janice it is likely a woman.. Not a precise method particularly for those people who identify themselves in a different way. Google scholar or Twitter often shows a picture and that is not fool proof either. 

The dilemma is in two ways: manual entries are open to errors in the first place. A six percent error rate is to be expected in any edit and anyone is kindly requested to fix what should be improved; Wikidata is rich in alternatives for male/female identifiers. The alternative is that we do not add a likely gender. This results in no awareness of the composition of the co-authors of an author. No awareness of the volume and balance of people who do not have an article yet.

I think that a male author with only male co-authors is problematic in and of itself. Quite often it is just that no attention was given to female co-authors so I often remedy this by giving attention to them. I add them to Wikidata, look for an ORCiD identifier, a Twitter handle a Google scholar profile. The effect is not only apparent for the male author, but it has an effect on all the co-authors for the newly registered author.

The issue I have is, I see no solution for the dilemma of a gender balance in Wikidata. What I do know is that Wikidata is a collaborative project and anyone is kindly requested to make it as good as it can be.

Thanks, GerardM

@Wikimedia needs your support because what it does, what we do is not enough

 An article in the "Daily Dot" insists that Wikimedia has plenty of money.  This is based on the growth of Wikimedia budgets and yes, it has grown substantially over time. Particularly the English Wikipedia provides a lot of content and serves some 50% of the Wikimedia traffic. 

When people analyse its content, it becomes problematic. Even though its content is referenced, many of the references are old and could do with new insights that science brings on a regular basis. The content is male oriented and thanks to projects like "Women in Red" it has improved substantially but not enough. 

We know all mayors of Denver and we do not know National government ministers of African countries. Lists are to be maintained on EVERY Wikipedia, English consensus insists, and they are not properly maintained as a result. Not even on the English Wikipedia.

Money buys you things. When you donate to the WMF, you gain a sense of ownership. That is important; we may not need more money but we do need a sense of ownership in India, Columbia, Nigeria and Guinea. When the other 50% of Wikimedia traffic takes ownership away from those who had enough, we find topics with more real world relevance. Commons becomes usable in the other 299 languages and we seek out these 299 communities to make it work for them.

Given that we don't do enough for 300 languages, given that we can do much better, I will argue that Wikimedia needs more support, even money.

Thanks, GerardM

It is not a list when it is the result of a query

A list is a presentation of data. When a list is maintained manually, the list IS the data, when the data is the result of a query, it REPRESENTS the data.

The difference is quite important. Changing the information in a query is in the definition of the query, changing the data is a matter of re-running the query. Changing the information in a list is a lot of work and therefore there is no integrity in the data itself, it is always potluck what quality the data is.

In the Wikipedia world, Listeria is king of the queried lists. For some its use is controversial but things are changing for the better. Projects like Women in Red use Listeria a lot, their work is possible because people add notable women in Wikidata. The queries work on the basis of awards, professions, nationality enabling volunteers to write the articles they care to write. This works because once an article is written they are automagically removed from the lists.

On the English Wikipedia consensus has it that manual lists are to be preferred. However, emperically the quality of automated lists perform better {{REF}} and as data in Wikidata does not suffer from "false friends" even the support for "red links" is vastly superior.

There is no point in anecdotal evidence who is best. When the English Wikipedia has a black link for Stephen Fleming on its page for the Spearman medal first, it is an obvious start for a new item on Wikidata that is more than just a person who won the Spearman medal. It then becomes a target for lists of the special interest groups who aim to cover "their" subject matter well.

The next stage of the acceptance of lists relies on the realisation that "consensus" does not serve us well particularly when it trumps established facts. It will serve us well in politics and, in what Wikimedia projects could be.
Thanks,
      GerardM

Bias in @Wikidata and a SMART approach

When at the WikidataCon quality was presented, it was rated from 1 to 5. This approach has its own bias because it does not consider what may not be there. What is not there can be made visible using assumptions like: "a university has more than one employee" (employee includes professors) and, every country has at least one university..

The bias in Wikidata starts with the way it is mostly used and consequently how it is taught. People are shown what Wikidata looks like, immediately followed up with training in the use of query and the use of tools. At every level it takes considerable skills to make a use of Wikidata. The first hurdle to overcome is to understand the data in a single item. When your language is not English you are toast. This is Cape Town in Newari and this is a useful presentation using Reasonator. With Reasonator the information is easy to digest and adding missing labels is just one click away.

The second hurdle is knowing what bias it is you want to remedy. For a known bias like the gender gap, the Women in Red have lists of missing Wikipedia articles. A Wikidata gap is expressed by the absense of data. Listeria lists are great at that.. These are all the universities of Africa.. If you do not get the extend of what we miss, you have some thinking to do. When you apply this principle to the science of Africa, you find a lot of lists and the biggest issue remains; missing lists.

When you tackle a missing subject like I did for the "Affiliates of the African Academy of Sciences", you will find a source as a reference for the group and a reference on every affiliate. To ensure that the data is relevant and actionable, I added all of them, linked them to ORCiD and/or Google Scholar enabling SourceMD to link them to their papers. I added nationality because this may trigger inclusion on the Women in Red lists and when it was obvious, I added employers so that they may be included as a scholar on African University lists..

When we as a movement want to fight bias, we have to consider the use of lists and particularly Listeria list to show the developments of a subject. With lists available on many Wikipedias, it becomes possible to gain traction on what we miss. This approach is distinctly different as it acknowledges the need for more support for item based editing and it makes the point that missing data is a quality issue that needs to be addressed as a fundamental issue.
Thanks,
      GerardM

Bulk uploads linked to @ORCID_Org and others, then what

Bulk uploads to @Wikidata happen all the time, for instance the latest medical publications. They result in links to existing scholars and new authors. The question: "then what" was raised on Twitter and in the question was the assumption of a quantitative reply.

When such data is imported in Wikidata it does not fall into a vacuum. Many notable scientists are already known because they have a Wikipedia article and because they are linked to "authorities" like ORCiD, VIAF, Google Scholar and many others. The result is a "Scholia" for a scholar and it includes all the known papers, the co-authors, dates of awards. This is one example of a scholar without a Wikipedia article.

Scholia is a very important tool as it enables more work on scholars. The display of co-authors for instance show their gender. Orange for women, blue for men and white when it is not known. Many people are involved in "Women in Red" writing new articles about lady scientists. On the project page of Women in Red you will find lists that are the result of queries run on Wikidata. This is why adding gender info is so important. Notability may be inferred from the awards people received, notability gains relevance when it does not stand alone. This is why a link to "authorities" establish the necessary notability for a Wikipedia article. Objectively this is best presented in a Scholia like the example of Elizabeth Barrett-Connor.

When attention is given to a scholar like Mrs Barrett-Conor, arguably the "ungendered" scholars are relatively new to Wikidata and typically incomplete. There is a tool for that; SourceMD adds missing papers and links to existing papers. It also adds links to known authors and adds missing authors. The effect is a network of information that is increasingly rich. Arguably this is a bulk upload in its own right but the origin is a different one.

Presentations on topics like awards, organisations, topics and much more are available from the Scholia tool. In such a presentation it shows what we have and given that Wikidata is a wiki, there is more to know. Award winners may be enriched with authority information, they may be linked to papers. Frequent publishers to a topic may have co-authors that could do with some TLC.

In answer to the original question; bulk uploads invite additional work, the data is enriched and becomes increasingly relevant.
Thanks,
       GerardM

#GenderGap - The Gineta Sagan Award (and others)

The Ginetta Sagan award is conferred by Amnesty International USA. It is an annual award, the last recipient according to Wikidata when I looked at it received it in 2014, English Wikipedia has the award as part of the article on Ginetta Sagan and has information including 2017 (when you read the texts, you will find how notable these people are and, by inference the people without an article).

Arguably, there is a lack of balance between the number of men and the number of women having an article in any Wikipedia. This is known as the "gender gap" and the "women in red" project works to great effect to improve that balance. There is no lack of fine notable ladies who have no article.

I am really happy to present two queries. The first query shows women who won an award with no article at all (2502 results). The second shows women who won an award with no article in the English language (29083 results).

Let these women be an inspiration to you.
Thanks,
       GerardM

#Diversity - A Woman's hall of Fame

Wikipedia has a category of some 40 Women's hall of Fame. They are women from the past and the present that are seen as exemplary. For all the women who have an English article there is now a statement indicating that they are seen as such.

For many women who are on these lists there is no article. Obviously when the objective is to have quality articles on notable women, it is good when there are lists with articles that could be written.

There are such lists and the best thing is they is some form of automated maintenance. The Women in Red project has such lists. Many of their lists find their basis in Wikidata and it is therefore possible to add people to their lists by adding key data.

All the women who have articles are now known as such, The next thing is to add the missing articles, the red links. So far I have added items for them one by one and stated what they are known for. Obviously this is a stub. More information is needed to state what they are known for, where they lived, why they are notable. It is not only how you enrich the data it is also how you increase diversity.
Thanks,
      GerardM