Evaluation Data Quality

Evaluation quality depends on the cases, labels, source rights, and review history.

A large dataset is not useful when the task contract or provenance is weak.

Decision

Can another reviewer explain where each case came from and why its label is correct?

Keep external, self-reported, synthetic, and platform-verified records in separate classes.

Source

Record origin

Keep the source identity and capture time.

Rights

Confirm allowed use

Store the basis for evaluation use.

Label

Use one rubric

Document reviewer agreement and corrections.

Version

Preserve history

Do not rewrite prior evaluation results.

1. Control the comparison

Use these controls before you collect or compare results.

Contract

Start from the evaluation contract

Collect only cases that measure the named behavior and its known risks.

Define case eligibility

Name required metadata

Reject unrelated volume

Evidence

Keep provenance complete

Each case needs a source, capture method, rights class, and transformation record.

Use stable source identifiers

Record transformations

Keep synthetic markers

Review

Control label quality

A label is evidence only when reviewers use the same decision rule.

Write a label rubric

Measure disagreement

Keep adjudication reasons

Decision

Protect the holdout

Separate optimization examples from cases used for final release decisions.

Restrict access

Check duplicate content

Monitor contamination

2. Use the procedure

Complete each step in order. Stop when a required input or control is missing.

Define case eligibility

State the behavior, source classes, and exclusion rules.

Capture provenance

Record the source, rights, time, and transformation.

Write the label rubric

Define each label and its decision boundary.

Review a sample

Measure agreement before you label the complete set.

Create the split

Separate iteration cases from protected holdout cases.

Version the manifest

Record additions, corrections, exclusions, and reviewer decisions.

3. Keep the evidence

Store enough evidence for another reviewer to repeat the decision.

Record	Required evidence	Failure signal
Source	Stable identifier and capture record	The origin cannot be verified
Rights	Use class and restriction record	Evaluation use is not authorized
Transformation	Code or recorded operation	The source cannot be reconstructed
Label	Rubric, reviewer, and adjudication	Label meaning changed silently
Split	Membership and access control	Holdout cases entered optimization

4. Keep the product boundary

Do not treat synthetic data as observed user behavior.
Do not mix unknown-provenance records into the rights-cleared corpus.
Do not use public benchmark data as a private holdout.
Do not delete corrected labels. Preserve the prior version and reason.

5. Apply this on Versalist

Attach cases to a Challenge

Keep each evaluation case connected to its task and rubric.

Read Challenge Design

Apply the evidence policy

Review the current evidence rights and access rules.

Read result documentation

Release gate

Make the decision explicit

Use the dataset only after provenance, rights, labels, and holdout controls pass review.

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