Thursday, February 27, 2020
Balancing arguments - Gender and the #Wikimedia projects
So far so good. At Wikidata other things are at play. It is vital to understand that Wikidata items are not so much about an individual, an item. When recipients of an award are included like for "Member of the Hassan II Academy of Sciences and Technologies". There is often nothing more than Moroccans that received an award because a source says so. Determining a gender relies on googling for images of the person and when the name is decidedly male like Omar, Hakim, Mustapha the gender is implied.
Why include a gender? Because projects like Women in Red rely on prospects to write articles about. Because tools like Scholia do express what we know about all the recipients of an award.. It tells us that there are currently two ladies known and 22 gentlemen. We know nothing of their work because the bias against Africa is staggering and because performance for inclusion at Wikidata is abysmal.
The arguments why we should not include gender is often based on what people expect; "Wikidata contains large sets of data and consider that it makes no statistical difference one way or the other". The reality however is that when you consider the use of data in for instance Scholia, the subsets are small. One more fine lady makes a statistical difference.
When people write about a person for a Wikipedia, they do get to know the person, they have multiple sources at hand. At Wikidata not so much. One purpose of adding people is to nibble away at our bias.
Requiring sources to indicate gender is what takes away the usefulness of the data and is counter productive when we are talking bias. For me it is a Wikipedia argument, an article based argument and it is counter productive to translate it to the set based approach of Wikidata.