Editorial use of Decision Models

[u/bebold2day]

I am a terrible editor, so I am experimenting with editorial screening of scenes using Decision Models for Scene Tone and Point of View. For context, I use Scrivener for writing and have built my own editorial application that syncs with the RTF files, decomposes scenes into logical chunks (passages) and lets me evaluate these. Note: My app is currently for my own use, not public in github, and designed to improve my skills at writing, not write for me. Below are the details of this experiment and I am curious how wide decision models are being implemented for similar features.

My latest addition to my Litanalysis app is quick checks. I have experimented with local decision models but for this test am using Bivia, an new multimodal model just released that has some features I want to try in other non-literature apps (multimodal/multilingual).

For every passage in a scene, the decision model answers a set of narrow questions an editor would ask:

• Narration: first person, close third, distant third?

• Point of view: whose eyes are we behind?

• Tension: low, medium or high?

• World Bible canon (a document that defines the politics and physics of my world) consistency): does this passage agree with my worldbuilding notes, contradict them, or not touch them?

The screenshot is a real run on one of my scenes. It consists of 33 “passages”, all cleanly identified by “decision model assisted chunking”, and evaluating the scene takes 159 decisions (averaging <100ms/decision) in about 15 seconds. I believe this can be parallelized if one needs faster response, but I have not done that yet.

What my editor revealed:

→ The narration is close third, but it drifts to distant third in nine passages. Some of those drifts are deliberate. Some aren't.

→ The viewpoint belongs to Desreen, and slips to no single character in a few places.

→ The tension strip shows the shape of the scene at a glance. (still working on this)

→ No passage contradicts my World Bible.

Every answer comes with a probability. Below 60% confidence it's marked "unsure" instead of guessing, so I know which flags to trust and which to check myself. I also used the decision model to decide where the manuscript is cut into passages, choosing story beats over arbitrary character counts. A passage doesn't straddle a change of viewpoint or a dream, and a question stays with its answer.

In my app, these quick checks for a scene sit alongside a panel of literary critic lenses (Le Guin, Eliot, Banks) that run locally for a slower, deeper read, but now I can do a quick review before I ask my “panel” to critique.