highlight · part i · 4

Your codebook becomes someone else's starting point.

Share the URL. Another lab can run it, refine it, or extend it.

your codebook

intervention_type · p_below_05 · is_blinded
allocation_concealed · …

Lab A

runs it on a new corpus

replication

Lab B

adapts three fields for a new cohort

refinement

Lab C

extends with five new fields for a new domain

generalization

When another lab runs your codebook on their corpus, either outcome is useful. If it handles their documents cleanly, that's replication. If their documents expose edge cases yours didn't, the codebook gets refined. Both depend on someone else running it.

A codebook is only a method if someone else can run it.

@karlrohe

A published codebook becomes somebody else's starting point, quickly. It is not only a record of what you did.

Moving a coding scheme between labs used to mean moving the lab's training materials.

Handing off a codebook does not require lab training materials. You give a pointer to your codebook, and another lab can run it. That is the whole handoff.

They can run it on Data Mint, on the same models. Or they take the codebook and pump it into the model they prefer. It is plain text, so it travels either way.

The loop of replication, refinement, and generalization gets much tighter when the codebook is the thing that matters.

see alsopublish the codebook— Why the codebook, and not the model, is the artifact worth reporting.what is a codebook— Plain text is what makes a codebook portable.where readers disagree— Their documents will split the readers where yours never did.refining one codebook— What the other lab does next, worked through on a single field.

Share the method, not just the data. datamint.ing/learn/highlights/codebooks-travel