Inverse Editor-Author Power

New authors have fairly weak power. Editors can exert their power to:

  1. streamline the story such that it flows more smoothly
  2. cut out dense detailed descriptions that take the reader from the story
  3. their experience at what it takes to make a book become a bestseller gives them authority

The trick is when the author becomes a proven bestseller, the editor becomes weaker. You can look at the books in a series like Game of Thrones and see how the editor became weaker as the book lengths ballooned.

  1. A Game of Thrones 726 pages
  2. A Clash of Kings  761 pages
  3. A Storm of Swords  973 pages
  4. A Feast for Crows + A Dance with Dragons 1761 pages

Tom Clancy follows this model as well.

Guard Dead Paper?

Seth said, “What we don’t need are mere clerks who guard dead paper.” Whenever, I read “mere”, “only”, or “just” as a descriptor, it makes me sad someone (even me) relies on obvious straw men.

Librarians already do more than guard dead paper. It just makes it easier to knock them down and kick them while they are down to portray them as such. Of course, the point is that Seth wants to see “… a librarian who can bring domain knowledge and people knowledge and access to information to bear…” which describes… every… librarian… I have ever known going back to age 5. Maybe growing up in and working in libraries gives me a different perspective than Seth?

The librarians I know…

  • Help patrons learn how to find information.
  • Learn quickly what the patron knows and how to connect the dots.
  • Have a master’s or doctorate in librarian (information) science but an undergraduate in something else because almost no where offers a bachelor’s in it.

How about this? “What we don’t need are mere scribes who throw words on paper. I want to see an author who can bring domain knowledge and people knowledge and communicate  information.” Yeah. Still just as demeaning without being at all helpful.

I Write Like Me

Check which famous writer you write like with this statistical analysis tool, which analyzes your word choice and writing style and compares them with those of the famous writers.

Not trusting a single sample, I tested fifteen writing samples including stories and blog posts (excluding those with block quotes). The Cory Doctorow result was the most common at six.

I write like
Cory Doctorow

I Write Like by Mémoires, Mac journal software. Analyze your writing!

I also received David Foster Wallace (3), Arthur Conan Doyle (3), J.K. Rowling (2), Isaac Asimov (1).

There was a clear pattern to the results.

  1. Cory Doctorow: Topic was work. Analyzer probably keyed on the dispassionately objective word choice.
  2. David Foster Wallace: Topic was my personal life. Analyzer probably keyed on me portraying the  absurdities.
  3. Arthur Conan Doyle: Topic was adventure story originated in high school. I probably thought too much like Sherlock Holmes then.
  4. J.K. Rowling: Topic was also adventure story composed in early college. I probably thought too much like Harry Potter then.
  5. Isaac Asimov: Topic was science. Its hard not to use scientific jargon when writing about science.

That there would be a difference between my high school and college story writing was interesting. The difference depending on whether I was writing about work, personal, or science was also interesting. I would have liked to see almost every sample I chose of my writing to reflect a single author. Otherwise, it seems results skewed towards word choice not style.

From the developer, Dmitry Chestnykh on how this works.

Actually, the algorithm is not a rocket science, and you can find it on every computer today. It’s a Bayesian classifier, which is widely used to fight spam on the Internet. Take for example the “Mark as spam” button in Gmail or Outlook. When you receive a message that you think is spam, you click this button, and the internal database gets trained to recognize future messages similar to this one as spam. This is basically how “I Write Like” works on my side: I feed it with “Frankenstein” and tell it, “This is Mary Shelley. Recognize works similar to this as Mary Shelley.” Of course, the algorithm is slightly different from the one used to detect spam, because it takes into account more stylistic features of the text, such as the number of words in sentences, the number of commas, semicolons, and whether the sentence is a direct speech or a quotation.

Bayesian filters I’ve seen given an item a score to how likely an item is something. I would like to see the strength of the scores, including distributions, and comparison of a given result to other close results. Guess I am just someone who wants to know why?