narRaters 0.3.9

AI-assisted narrative processing with human-screening — pipeline + web UI for audio/text narratives.

narRaters

Turn complex narratives into structured, reviewable data — with a web UI at every step

GitHub: github.com/xianNeuro/narRaters · PyPI: narraters

🏠 Project home · 📦 PyPI · 📖 Tutorial (PDF) · 🐛 Issues · 💬 Feedback

What is narRaters?

narRaters (narrative + raters) is an open-source software on GitHub (xianNeuro/narRaters) that helps process complex narratives (e.g., audio book, text-based stories, interviews, conversations, etc.) for memory, language processing, causal reasoning, and LLM research.

Imagine you ran a memory study: participants listened to a story, then recalled what they remembered (spoken or typed). Before you can analyze memory, you need structured data — what happened in the story, what each person recalled, and how those pieces connect.

narRaters helps you get there. It runs common narrative-processing steps (transcribe audio, split a story into events, clean up recall text, parse recalls into clauses, match recalls back to story events, rate causal links between events) and gives you a web interface to review and fix outputs before exporting.

Works for audio or text, stories or other long narratives (including movie annotations), and human-only or human-vs-LLM workflows.

You have…	narRaters helps you…
Story audio or transcript	Transcribe it and break it into numbered events
Participant recall files	Correct spelling, split into clauses, and match each clause to story events
A segmented story	Rate causal links between event pairs (did event A lead to event B?)
Automated or AI outputs	Screen and edit them in the browser, then export signed-off files

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Get started in 3 steps

1	Download & open Get the ZIP, unzip, and double-click narRater.app (macOS) or narRaters_installer.bat (Windows). Needs Python 3.10+.
2	Pick your pipeline Your browser opens to the pipeline builder. Drag in only the steps you need (e.g. segment → match → causal rate). Bundled demo data is already loaded so you can explore immediately.
3	Run, review, export On the dashboard, click a cell to run a step. Open the magnifying-glass icon to inspect results, edit in the browser, and export when you are satisfied.

Or via terminal (Python 3.10+; no ZIP download). Run from the folder that contains your data/ and output/ directories (or set NARRATERS_PROJECT_ROOT to that path):

python --version                              # must show 3.10 or newer
python3 -m pip install narraters --upgrade    # wait for “Successfully installed”
cd /path/to/your/project                      # folder with data/ and output/
narraters serve                               # browser opens to the pipeline builder

Then continue with steps 2–3 above — pick your pipeline, run steps on the dashboard, review, and export.

First time? Follow the illustrated Tutorial PDF or see Installation and Troubleshooting.

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See the app

① Build a pipeline  →  ② Dashboard  →  ③ Rate causal links

① Pipeline dashboard See every subject/story, run steps, and open results. Green = done; click a cell to process.

② Event segmentation Move the cursor through the text and click to drop boundary bars. Toggle binary or 1–5 strength (bar colored blue→red).

③ Recall matching Story events on the left; recall segments on the right. Assign which events each recall segment refers to.

④ Causal rating Click a grid cell to rate how strongly one story event caused another (0–3 scale).

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Table of contents

• What is narRaters?
• Get started in 3 steps
• See the app
• Installation
  • ZIP download (double-click launcher)
  • PyPI (terminal)
  • Alternate install (command line)
  • Using the web UI
  • Troubleshooting
• Where to put your data
  • Example input/output data
• Pipeline overview
• Command-line pipeline
  • Step 1 — transcribe
  • Step 2 — segment
  • Step 3 — correct
  • Step 4 — parse
  • Step 5 — match
  • Step 6 — rate
• Contributing tab
  • Research background
  • Prompt templates
  • Acknowledgements
  • Author
• Library / Python use
• Project layout
  • Folder structure
• Further reading
• Acknowledgements
• License

On GitHub, README, Contributing (research background, prompt templates, acknowledgements, author), and License are the tabs in the bar above. Use this table of contents or the Outline menu (list icon, top-right) to jump between README sections.

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Installation

Needs Python 3.10+. Windows: check “Add python.exe to PATH” in the Python installer. If anything fails, see Troubleshooting.

ZIP download (double-click launcher)

1. Download the ZIP (v0.3.9) and unzip it — or on the GitHub repo page use green Code ▾ → Download ZIP for the current main branch. You'll get narRaters-0.3.9, narRaters-main, or narRaters (if you used git clone).
2. Launch: macOS — double-click narRater.app. Windows — double-click narRaters_installer.bat. Linux — in Terminal, cd into the folder and run bash install.sh.
3. Your browser opens http://127.0.0.1:5000/pipeline-config with bundled examples. Put your data in data/. Restart later by double-clicking the same launcher.

macOS Gatekeeper or quarantine issues? See Troubleshooting.

PyPI (terminal)

python --version                              # must show 3.10 or newer
python3 -m pip install narraters --upgrade    # use python3 -m pip, not bare pip
narraters serve                               # browser opens to the pipeline builder

On first launch, example data/ and output/ folders are copied into whatever directory you run from (unless you already have a project folder, or set NARRATERS_PROJECT_ROOT). Package: narraters (all lowercase). For launchers and the tutorial PDF, use the ZIP install above.

Full PyPI setup (venv from scratch)

python3 --version        # must be 3.10 or newer
mkdir -p ~/narRaters-demo && cd ~/narRaters-demo
python3 -m venv .venv
source .venv/bin/activate     # Windows: .venv\Scripts\activate
python3 -m pip install --upgrade pip
python3 -m pip install narraters --upgrade
narraters serve

Alternate install (command line)

git clone + install.sh (project folder with bundled examples)

# macOS / Linux
cd ~ && git clone https://github.com/xianNeuro/narRaters.git && cd narRaters && bash install.sh

:: Windows
cd %USERPROFILE% && git clone https://github.com/xianNeuro/narRaters.git && cd narRaters && narRaters_installer.bat

This is what narRater.app does under the hood, just without the click. git: command not found? On macOS: xcode-select --install. On Windows: install Git for Windows.

Optional extras (Whisper, cloud APIs, local Gemma, etc.)

Inside the project folder, with the venv activated:

python3 -m pip install -e ".[audio]"     # Whisper transcription
python3 -m pip install -e ".[api]"       # Anthropic / OpenAI
python3 -m pip install -e ".[nlp]"       # spaCy segmentation
python3 -m pip install -e ".[grammar]"   # grammar checker
python3 -m pip install -e ".[local-llm]" # local Gemma
python3 -m pip install -e ".[match]"     # rmatch
python3 -m pip install -e ".[all]"       # api + match

PyPI users: python3 -m pip install "narraters[audio]", etc.

Heavy methods (audio, local-llm, match) pull multi-GB packages — the app shows a RAM/disk preflight before downloading. Ollama (local Gemma): install Ollama, then ollama pull gemma4:e4b. API keys: copy .env.example to .env and edit (see SETUP_API.md).

Developers

install.sh already does an editable install. To work on the codebase:

git clone https://github.com/xianNeuro/narRaters.git
cd narRaters
python3 -m venv .venv && source .venv/bin/activate && python3 -m pip install -e .

Build the standalone macOS app for icon testing: bash packaging/macos/build_app_bundle.sh. Build the slim repo-root launcher: bash packaging/macos/build_repo_app.sh.

Using the web UI

The app runs at http://127.0.0.1:5000. First visit opens pipeline configuration; if you already saved a pipeline, you land on the dashboard.

Screen	Route	What you do there
Pipeline setup	/pipeline-config	Drag steps into Pipeline Flow, set per-step folders, enter a rater name (or 🎲). Continue saves config and opens the dashboard.
Dashboard	/	Grid: rows = subjects or stories, columns = steps. Click a cell to run that step (pick method / model / prompt when offered). Batch runs one step across all rows.
Detail view	/subject/… or /story/…	Tabs per step for one row. Use the version dropdown to compare automated output vs your {id}_{ratername}-edit saves, then edit and save.

Flow: setup → dashboard (bulk runs) → open a row to inspect, hand-correct, or compare versions.

narraters serve options

Flag	Default	Purpose
--port	5000	Another port if 5000 is busy
--host	127.0.0.1	Bind address; use 0.0.0.0 only on a trusted network
--no-browser	off	Do not open a browser tab (SSH, headless)
--debug	off	Flask debug / auto-reload while hacking on the server

narraters serve --port 8080 --no-browser

Before a step would load Whisper, Gemma via Ollama, rMatch, or other heavy local models, the UI runs a RAM / disk preflight and may suggest a lighter method (rules, test, clause) if the run looks unsafe for your machine.

Troubleshooting

If you see…	Do this
Python 3.10+ required	Install Python 3.10+, close and reopen any Terminal, run again.
Blank page on localhost:5000	Visit http://127.0.0.1:5000/pipeline-config instead (IPv6/IPv4 quirk on some Macs).
macOS: Gatekeeper / “cannot check for malicious software” / no Open in the right-click menu	1. In Finder, try control-click narRater.app → Open, then confirm Open if the dialog offers it — Apple’s Gatekeeper overrides. 2. If that path is missing or still blocks: System Settings → Privacy & Security → scroll to Security — after a failed launch, macOS often shows narRater was blocked (wording varies) with Allow Anyway or Open Anyway; click it, enter your password, then launch narRater.app again (that button may only appear for a limited time after the block). 3. Downloaded folder still quarantined: in Terminal, xattr -dr com.apple.quarantine /path/to/narRaters-main, then try 1 or 2 again.
macOS: “narRater couldn't find the narRaters project folder”	macOS App Translocation ran the app from a temp copy. Run xattr -dr com.apple.quarantine ~/Downloads/narRaters-main (adjust path) and double-click again, or use git clone install.
Windows: SmartScreen warns about narRaters_installer.bat	Click More info → Run anyway.
Port 5000 already in use	The installer auto-tries 5001–5010 and prints the URL. To free 5000: macOS → System Settings → General → AirDrop & Handoff → turn off AirPlay Receiver.

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Where to put your data

After installation, place files so the paths match what you configured on the pipeline page (defaults below are relative to the project root). You can remap any step’s input/output folders there without moving data.

You have…	Put it in…	Format / naming
Story transcript (text)	data/2_story_transcript/	{story}.txt — plain UTF-8 text, one story per file
Story event list (pre-segmented)	data/3_story_events/	{story}_events.xlsx — columns event, story_texts
Subject recall text	data/5_recall_texts/	{subj_id}.txt — e.g. the_siren_sub-01.txt
Story audio (optional, Step 1)	data/1_story_audio/	.wav / .mp3 / .m4a, named by story
Recall audio (optional, Step 1)	data/4_recall_audio/	.wav / .mp3 / .m4a, named by subject

Outputs are written under output/ — one subdirectory per step (output/recall_corrected/, output/recall_parsed/, output/recall_rated/, …). A smaller alternate layout lives in demo/data/ (lighthouse story, three recall .txt files).

Example input/output data

The repository ships realistic sample inputs and outputs under data/ and output/ so you can see accepted naming and file types before adding your own study. Your private files in those folders stay untracked (see .gitignore); only the examples below are committed.

Stories: pieman_edited (story audio + transcript + events) and the_siren (transcript, events, two recall subjects).

Role	Folder	Example file(s)
Story audio (input)	data/1_story_audio/	pieman_edited.wav
Story transcript (input)	data/2_story_transcript/	pieman_edited.txt, the_siren.txt
Story events (input)	data/3_story_events/	pieman_edited_events.xlsx, the_siren_events.xlsx
Recall audio (input)	data/4_recall_audio/	Your own .wav / .mp3 / .m4a / .mp4 (not shipped publicly)
Recall text (input)	data/5_recall_texts/	the_siren_sub-01.txt, the_siren_sub-02.txt
Story transcription (output)	output/story_audio-transcribed/	pieman_edited.txt
Recall transcription (output)	output/recall_audio-transcribed/	the_siren_sub-01.txt, the_siren_sub-02.txt
Spell/grammar correction (output)	output/recall_corrected/	the_siren_sub-01.txt, the_siren_sub-02.txt
Parsed recall (output)	output/recall_parsed/	the_siren_sub-01_parsed.xlsx, the_siren_sub-02_parsed.xlsx
Recall ↔ events (output)	output/recall_rated/	the_siren_sub-02_rate-recall-test_mode.xlsx (method slug in filename)
Causal ratings (output)	output/causal_rated/	pieman_edited_causal-linguistic.xlsx, the_siren_causal-linguistic.xlsx

Quick try: after install, point a pipeline at the default folders above and run sentenceCorrect → textParsing → textMatching on the_siren_sub-01 / the_siren_sub-02, or open the bundled output/ files in Excel to inspect column layouts. Story pieman_edited is useful for audioTranscribe (large .wav) and causalRating on pieman_edited_events.xlsx.

File versioning is a core feature. Automated runs write {subj_id}_{method}.ext (or {story}_… for story-level steps); your hand-edited versions are saved as {subj_id}_{ratername}-edit.ext and never overwrite the originals. The web UI lets you switch between versions via a dropdown, and the -edit files are what you export for analysis.

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Pipeline overview

Six optional steps — use any subset, in any order. Each step can run automatically (rules, local models, or cloud APIs) and then be reviewed in the browser.

Plain English	Step ID	Input → output (typical)
Transcribe audio	audioTranscribe	audio file → text transcript
Split story into events	eventSegment	story transcript → numbered event list
Fix recall spelling/grammar	sentenceCorrect	raw recall text → corrected text
Split recall into clauses	textParsing	corrected recall → clause segments
Match recall to story	textMatching	recall segments + story events → rated matches
Rate event causality	causalRating	story events → cause–effect ratings

Full step reference (commands & folders)

In typical recall work, audioTranscribe / eventSegment target the story, sentenceCorrect–textMatching each subject recall, and causalRating the story event list — but text-only projects skip Step 1, and you can equally run just eventSegment + causalRating or sentenceCorrect → textParsing → textMatching. Every step is available from the GUI or narraters CLI, has a lightweight default method, and supports hand-editing afterward.

#	Step ID	What it does	Terminal command	Default in / out
1	audioTranscribe	Audio recordings → text (Whisper/WhisperX); story vs recall via audioScope or --kind	narraters transcribe	data/4_recall_audio/ (or data/1_story_audio/ with --kind story) → output/*_audio-transcribed/
2	eventSegment	Story transcript → numbered events	narraters segment	data/2_story_transcript/ → data/3_story_events/
3	sentenceCorrect	Fix spelling/grammar in recall text (no rewriting)	narraters correct	data/5_recall_texts/ → output/recall_corrected/
4	textParsing	Corrected recall → clause-level segments	narraters parse	output/recall_corrected/ → output/recall_parsed/
5	textMatching	Recall segments ↔ story events	narraters match	output/recall_parsed/ + data/3_story_events/ → output/recall_rated/
6	causalRating	Causal strength of every story-event pair	narraters rate	data/3_story_events/ → output/causal_rated/

For each step, the GUI runs the same backends as the CLI. Available methods, flags, and examples are under Command-line pipeline below.

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Command-line pipeline

Each of the six steps is a separate narraters subcommand with its own --method (and related options). Use the CLI for scripts, clusters, or reproducible runs—with or without the web UI, and with any subset of steps your study uses. General shape:

narraters <step> [--method METHOD] [--model MODEL] [-i INPUT] [-o OUTPUT] [--prompt-version VERSION] ...

Discover what's available at any time:

narraters --help                 # list all subcommands
narraters <step> --help          # step-specific options
narraters segment --list-prompts # list available prompt versions for a step
narraters segment --list-models  # list supported model identifiers

The method choices below are exactly those accepted by the CLI (src/narraters/cli.py).

Step 1 — transcribe (audio → text)

narraters transcribe --model large-v3 --timestamps          # recall audio (default)
narraters transcribe --kind story --model small              # story audio instead
narraters transcribe -i path/to/audio -o path/to/out         # custom directories
narraters transcribe --filter sub-01                         # one item only

Option	Choices	Notes
--model	tiny, base, small, medium, large-v2, large-v3	Whisper model name
--timestamps	flag	Also write Excel files with word-level timestamps
--kind	recall (default), story	Picks the conventional directories: recall = data/4_recall_audio/ → output/recall_audio-transcribed/; story = data/1_story_audio/ → output/story_audio-transcribed/
-i, --input	path	Input audio directory (overrides the --kind default)
-o, --output	path	Output directory (overrides the --kind default)
--filter	substring	Only transcribe files whose name matches this item id

Requires pip install "narraters[audio]" (or pip install -e ".[audio]" from a clone). Text-only projects can skip Step 1 entirely.

Step 2 — segment (story → events)

narraters segment --method clause
narraters segment --method api --model <anthropic-model-id> --prompt-version event_segment
narraters segment --method fine --input data/2_story_transcript/my_story.txt

Run narraters segment --list-models for the exact --model strings (Anthropic, OpenAI, and Ollama-backed presets).

Option	Choices	Notes
--method	clause, fine, coarse, api	clause needs no model; fine/coarse use spaCy if installed; api calls an LLM
--model	see narraters segment --list-models	Only used with --method api (Anthropic, OpenAI, or Ollama preset keys)
--prompt-version	see --list-prompts	Selects a template from scripts/prompt/event_segment*.txt
-i, --input	path	Single transcript file or a directory (else processes all)
-o, --output	path	Output directory (default: data/3_story_events/)

Step 3 — correct (spell / grammar fixes)

narraters correct --method rules
narraters correct --method gemma-ollama --ollama-model gemma4:e4b

Option	Choices	Notes
--method	rules, gemma-ollama	rules runs entirely locally with no model; gemma-ollama needs a local Ollama server
--ollama-model	e.g. gemma4:e4b	Local Ollama model tag (with gemma-ollama)
--prompt-file	path	Override the instructions file (default: scripts/prompt/spell_gram.txt)
-i, --input	path	Single recall text file
-o, --output	path	Output directory

Minimal corrections only — Step 3 fixes spelling/grammar errors and never rewrites or paraphrases.

Step 4 — parse (recall text → clause-level segments)

narraters parse --method rules
narraters parse --method ollama --model gemma4:e4b --prompt-version recall_parse_clause
narraters parse --filter-pattern sub-02            # process one subject only

Option	Choices	Notes
--method	rules, ollama	rules is the default (regex, no model); ollama uses local Gemma
--model	e.g. gemma4:e4b	Ollama model tag (with --method ollama)
--prompt-version	see scripts/prompt/recall_parse_*.txt	Prompt template name
-i, --input	path	Input directory (default: output/recall_corrected/)
-o, --output	path	Output directory (default: output/recall_parsed/)
--filter-pattern	substring	Optional filter to process a single subject

Step 5 — match (recall segments ↔ story events)

narraters match --test-mode                       # simulated keyword matching, no model/API
narraters match --method api --story-events data/3_story_events
narraters match --method gemma-ollama
narraters match --method rmatch                   # embedding matcher (requires [match])

Option	Choices	Notes
--method	test, api, gemma-ollama, rmatch	test is keyword-based, free, and always available; rmatch needs pip install "narraters[match]"
--story-events	path	Directory of {story}_events.xlsx (default: data/3_story_events)
-i, --input	path	Recall-parsed input directory (default: output/recall_parsed/)
-o, --output	path	Output directory (default: output/recall_rated/)
--test-mode	flag	Equivalent to --method test — simulated matching, no API calls

Step 6 — rate (causal relationships between event pairs)

narraters rate --method linguistic
narraters rate --method api --model <anthropic-or-openai-model-id> --prompt-version causal_rating
narraters rate --method manual                    # write an empty matrix for hand rating

Use narraters rate --help and the Step 6 model dropdown in the web UI for supported --model values when using --method api.

Option	Choices	Notes
--method	linguistic, api, manual	linguistic is rule-based (no model); manual scaffolds an N×N matrix to fill in by hand
--model	see web UI / provider docs	Only used with --method api
--prompt-version	see scripts/prompt/causal_rating*.txt	Prompt template name
-i, --input	path	Input file/directory
-o, --output	path	Output directory

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Library / Python use

from narraters import __version__, project_root
print(__version__, project_root())

Direct per-step imports are planned for a future release; for now, programmatic use should call the CLI via subprocess or import the modules under scripts/.

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Project layout

After unzipping, everything lives under a single narRaters/ project root. Paths, contents, and naming conventions:

Folder structure

narRaters/
├── README.md                    # This file — user guide & pipeline docs
├── CONTRIBUTING.md              # Research background, prompt templates, acknowledgements, author
├── LICENSE
├── narRater_Tutorial.pdf        # Illustrated web UI tour
├── narRater.app                 # macOS double-click launcher
├── narRaters_installer.bat      # Windows launcher
├── install.sh                   # macOS / Linux installer
├── pyproject.toml               # Package metadata & pip extras
├── SETUP_API.md, .env.example   # API key setup
│
├── data/                        # Inputs (see Where to put your data)
│   ├── 1_story_audio/           # Optional Step 1 — story audio
│   │   └── {story}.wav | .mp3 | .m4a
│   ├── 2_story_transcript/      # Story text
│   │   └── {story}.txt          # plain UTF-8, one story per file
│   ├── 3_story_events/          # Pre-segmented or segmented story events
│   │   └── {story}_events.xlsx  # columns: event, story_texts
│   ├── 4_recall_audio/          # Optional Step 1 — recall audio
│   │   └── {subj_id}.wav | .mp3 | .m4a | .mp4
│   └── 5_recall_texts/          # Recall text
│       └── {subj_id}.txt        # e.g. the_siren_sub-01.txt
│
├── output/                      # Pipeline outputs (one subfolder per step)
│   ├── story_audio-transcribed/ # Step 1 (story) — {story}.txt
│   ├── recall_audio-transcribed/# Step 1 (recall) — {subj_id}.txt
│   ├── recall_corrected/        # Step 3 — {subj_id}.txt
│   ├── recall_parsed/           # Step 4 — {subj_id}_parsed.xlsx
│   ├── recall_rated/            # Step 5 — {subj_id}_{method}.xlsx
│   └── causal_rated/            # Step 6 — {story}_causal-{method}.xlsx
│
├── scripts/                     # Pipeline backends (CLI & web UI call these)
│   ├── 1_audio-transcribe.py    # audioTranscribe
│   ├── 2_story-event-segment.py # eventSegment
│   ├── 3_spell-grammar-correct.py # sentenceCorrect
│   ├── 4_parse-texts.py         # textParsing
│   ├── 5_recall-rater.py        # textMatching
│   ├── 6_causal-rater.py        # causalRating
│   └── prompt/                  # LLM prompt templates (.txt)
│       ├── event_segment.txt
│       ├── spell_gram.txt
│       ├── recall_parse_clause.txt
│       ├── recall_rating.txt
│       └── causal_rating.txt
│
├── server/                      # Flask web UI
│   ├── web-interface.py         # Routes & subprocess orchestration
│   └── START_HERE.command       # macOS launcher script
│
├── templates/                   # Web UI HTML (pipeline, dashboard, subject/story)
├── static/                      # CSS, JS, app icon
│
├── src/narraters/               # pip package
│   ├── cli.py                   # narraters command entry point
│   ├── paths.py                 # Project-root resolution
│   └── runtime_install.py       # Bundled-example copy on first serve
│
├── helpers/                     # Shared utilities & smoke tests
│   ├── software_paths.py        # Canonical path resolution
│   ├── resource_preflight.py    # RAM / disk checks for heavy methods
│   └── test_*.py                # Pipeline validation scripts
│
├── docs/                        # GitHub Pages site & README assets
│   ├── index.html               # Project landing page
│   └── screenshots/             # README GIFs and static screenshots
│
├── demo/                        # Smaller lighthouse example
│   ├── data/                    # the_lighthouse transcript + recall texts
│   └── output/                  # Sample outputs for the demo story
│
├── developer/                   # Contributor handbook & tooling
│   ├── README.md                # Per-step I/O contracts & design principles
│   └── SETUP_API.md             # API key setup (developer copy)
│
└── packaging/macos/             # App bundle / DMG build scripts
    └── build_app_bundle.sh

Bundled examples: pieman_edited, the_siren — see Example input/output data.

Versioning: automated files use {id}_{method}.ext; hand-edited exports use {id}_{ratername}-edit.ext (never overwritten).

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Further reading

• Project home (GitHub Pages) — landing page for search and sharing.
• narRater_Tutorial.pdf — illustrated, click-by-click tour of the web UI; good next step after Installation.
• SETUP_API.md — API keys for Anthropic, OpenAI, and Hugging Face; which pipeline steps need which.
• scripts/prompt/README.md — prompt template conventions for LLM-backed methods.

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Acknowledgements

• Janice Chen for brainstorming the causal-rating step interface and for help testing and improving package functionality.
• Gabi Kressin Palacios and Dhruva Arekar for an additional method for the recall-matching step (matching human recall text to story events). See GabrielKP/rMatch for human-data–validated AI-assisted recall rating.
• Xiyu Li (Rita) for contributions to the recall_rating prompt development and for validating model performance on human recall data (commercial LLM APIs were close to human raters).
• Sebastian Michelmann for feedback on the event-segmentation step (see Michelmann et al., 2023).
• Colette Youstra and Quinton Covington for testing the app's manual-rating functions.
• Samira Tavassoli and Yuye Huang for help testing the app's segmentation and causal-reasoning functions.

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License

See LICENSE — narRaters Research and Non-Commercial License. Free for research, education, and other non-commercial use; commercial or for-profit use requires prior written permission. Contact [e-mail] for commercial licensing.