Wikipedia:WikiProject Countering systemic bias/Gender gap task force/Categorization

For reactions to the proposals see

List of categories needing attention

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  1. Category:American women activists and various subcategories
  2. Category:American women comedians catscan showing women potentially ghettoized
  3. probably most of Category:American women by occupation
  4. Category:Male feminists, Category:Gay feminists
  5. Category:American women painters and various subcategories
  6. Category:Women mayors.

How to categorize without bias

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Related to: Wikipedia:Categorization/Ethnicity, gender, religion and sexuality

The following instructions are intended to help categorize biographies without "ghettoizing" them (i.e. putting them in a gendered/ethnic/sexuality category while not being in an equivalent non-gendered/non-ethnic/non-sexuality based category with their peers).

  • To illustrate, we will choose a difficult case. Suppose you have a bisexual, African-American woman, who is a journalist, poet, and writer. She hails from Chicago, and is named Sue, and she writes poetry, essays and stories about her identity.
  • First, categorize Sue as if she didn't have any of those characteristics - i.e. as if she wasn't bisexual, wasn't African-American, and wasn't a woman. It's not about making her a white male, it's about imagining how she would be categorized in the current tree if she didn't have any defining facets at all - completely generic, with no adjectives at all. Look for categories that have no specifiers - where would you put her, just based on her job, nationality, and location?
  1. Category:Journalists from Illinois
  2. Category:American poets
  3. Category:Writers from Chicago, Illinois
  4. Category:21st-century American writers.
But, do remember to get as specific as possible - so Category:Writers from Chicago, Illinois instead of Category:American writers.
  • Next, add another facet - gender is probably best:
  1. Category:American women journalists
  2. Category:American women poets
  • Now, add another facet - like the fact that she's African-American. Go back to the tree, and add all relevant categories. Take account of the intersection of facets (ex: woman + African-American):
  1. Category:African-American journalists
  2. Category:African-American poets
  3. Category:African-American writers
  4. Category:African-American women writers
  • Then, add the final facet - Sue's identification as bisexual. Go back to the tree, and add categories - again taking into account any intersections/combinations:
  1. Category:LGBT journalists from the United States
  2. Category:LGBT writers from the United States
  3. Category:LGBT African Americans
  4. Category:Bisexual writers
  5. Category:Bisexual women

The end result can be displayed and sorted, for each job type, from generic to specific:

Journalist tree:

  1. Category:Journalists from Illinois
  2. Category:LGBT journalists from the United States
  3. Category:African-American journalists
  4. Category:American women journalists

Poet tree:

  1. Category:American poets
  2. Category:American women poets
  3. Category:African-American poets

Writer tree

  1. Category:21st-century American writers
  2. Category:Writers from Chicago, Illinois
  3. Category:African-American writers
  4. Category:African-American women writers
  5. Category:LGBT writers from the United States
  6. Category:Bisexual writers

Misc tree:

  1. Category:LGBT African Americans
  2. Category:Bisexual women

By starting at the top (generic) level, we are able to correctly categorize and not ghettoize. If you start instead at the more specific intersection levels, it is much harder to do. This is obviously a complex example - most examples will be somewhat easier.

Another interesting thing you might notice from the example are which categories don't exist at all - the whole tree is lopsided. We don't have Category:Bisexual journalists or Category:African-American women journalists or Category:African-American women writers from Chicago or Category:LGBT poets from the United States. This is the odd nature of the current tree, as created by many different editors with many different viewpoints - it is highly heterogeneous and not consistent - which is what makes "correct" categorization rather difficult, as you have to understand the tree, and embed the structure of the tree into the categories assigned to the person in the case of non-diffusing categories, such as those for ethnicity/gender/sexuality/religion.