02.4 / Lab notebook
← Lab indexIndus Script Statistical Analysis
Which sign patterns survive a statistical baseline in the Indus script?
I used AI to assist two tests on the Indus script. First I checked whether signs tend to occupy particular positions in an inscription. Then I asked whether published readings fit the data better than calibrated null models. The positional patterns replicated across corpora. The readings tested fell short of the per-sign benchmarks.
Scope and limits
The unreviewed preprint reports how signs are arranged. Their meaning remains unresolved.
Read the paperIndus Script / Workflow
How the study tests signs and proposed readings
- Match the signs
Align signs across the Mahadevan, ICIT-derived and CISI materials.
- Define the baselines
Use null models to estimate the patterns expected by chance.
- Test two questions
Measure positional structure and separately test how published readings perform against per-sign nulls.
- Check replication
Repeat structural tests in an independent corpus.
Worked explanationSeparate position from meaning
- Input
- A sign appears repeatedly in a particular position in one corpus.
- Decision / rule
- Compare the positional pattern with a null model, then test replication in an independent corpus. Proposed readings receive a separate test.
- Outcome
- A replicated positional pattern supports a structural result. It does not, by itself, establish what the sign means.
Scope of this exampleThis explains the unreviewed study’s method, not a new statistical run or a decipherment.
Try it locally.
Run these commands from a checkout of the project. The repository includes setup details and prerequisites.
python3 -m venv .venv
.venv/bin/python -m pip install -r requirements.txt
.venv/bin/python tests/smoke_test.py