A Big Hugo Finalist List

I am primarily a machine that turns carbohydrates into spreadsheets. I’m OK with relational databases but they are not my natural territory. I prefer my data in great big lists of everything and if I want lots of little tables then I’ll pivot it.

I’d started rationalising some of the throw-away spreadsheets I made for blog posts and I’d started with the sheet I’d made for my Hugo Window posts [https://camestrosfelapton.wordpress.com/2020/06/21/one-way-the-mid-1980s-did-change-in-the-hugo-awards/ ] As Google Sheets now has pivot tables and other features that make it a reasonable alternative to Excel I’ve put all that data there. You can see for yourself here https://docs.google.com/spreadsheets/d/1lL9bm3I7yrkKxSAZwN1NhWr6OB8-s10IkV1g_MSSGXY/edit?usp=sharing

Now, I was also working on a new sheet for the IGNYTE awards (which I need to get back to) but I got diverted by a query over Twitter, which was sufficiently interesting and which led to some extra oomph to the Google Sheet listed above.

Yasser Bahjatt is a fan from Saudi Arabia who was part of the JeddiCon bit and is a Guest of Honour at FIYAHCON. He’s also trying to collate Hugo data and looking at diversity across awards. I said I’d share what I had and see what I could add to my great-big-spreadsheet.

I started asking a few other people who had collected Hugo data what they had (and thanks in particular to ErsatzCulture for tips on getting stuff out of ISFDB efficiently) but then life and work got in the way. There’s other people I meant to hassle but time etc…(so don’t feel left out that I didn’t bug you!)

I’m still think about categories that could help inform analysis and track diversity and inclusion in awards. One issue is that some of the most relevant fields are not easily collated – in particular ethnicity. A second issue is that once you step away from data that is easily found on Wikipedia or an author’s public bio, you start shifting towards collecting personal data about living individuals, which is an ethical and legal minefield.

Here’s a list of categories and some thoughts on them:

  • Year: Already have this in my sheet (note my great big sheet only has the main story categories)
  • Award: I have Novel, Novella, Novelette and Short Story but not other Hugos
  • Name of Nominee: I had names but I augmented this with ISFDB data to help match pseudonyms
  • Number of nominations: available and I’ll add this progressively. I do have the number of times the author was a finalist as well.
  • Number of Votes: available and I’ll add this progressively.
  • Gender of Nominee: I’ve done pronouns instead because it is quicker to collect and data is more reliable. I’ll probably need an extra column for the pronouns of the named author v the pronouns the author uses aka James Tiptree v Alice Sheldon.
  • Year of Birth: Imported from ISFDB
  • Age of Nominee when nominated: Age at finalist based on the ISFDB data. Approximate as I’m not taking time of year into account (i.e. which side of the award announcement their birthday fell on)
  • Ethnicity of Nominee: Tricky, tricky. I need to think about this more because you really need a personal identification of ethnicity here and it’s relative to the country the person lives in and the the general USA-context of the Hugo Awards.
  • Religion of Nominee: Less ambiguous than ethnicity and sometimes public but one of those categories that is on the border between public data and personal/private data.
  • Country of Nominee: ISFDB has country of birth (which can be complex for pre-WW2/WW1 births) but nationality may be more relevant.
  • Original language of the Nominated work: ISFDB has this.
  • Nominated language of the Nominated work: I think all the Hugo finalist stories have been nominated in English translations, so possibly redundant.
  • Profession(s) of Nominee: This is interesting and I’ve looked at this kind of info before when looking at the number of academics nominated for Hugos. It’s largely on the public side of the public/private divide I think. However, it’s very mutable and really what is most interesting would be “profession at first time finalist”, so that the column isn’t just “writer”

Anyway, a work-in-progress.

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10 responses to “A Big Hugo Finalist List”

  1. I’m OK with relational databases but they are not my natural territory. I prefer my data in great big lists of everything

    But it’s much more fun to have big lists of everything when one can write some good old-fashioned sql to query those lists!

    Liked by 2 people

    • +1

      I absolutely adore relational databases. There’s an incredible beauty and economy in them.

      Like

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