Purpose
Give teams a short, practical routine for keeping evaluations accurate, so calibration compounds correctly instead of drifting. See Calibration.Practices
1
Keep roles and rubrics current
Retire stale roles and keep each role’s rubric aligned to what the job
actually needs. See How to Set Scoring Weights.
2
Keep candidate data clean
Review incoming candidate records for accuracy and fix obviously bad data
before evaluating. See View and Manage Candidates.
3
Use consistent pipelines
Apply the same pipeline and stages for comparable roles, so scores stay
comparable across teams. See How to Design Your Hiring
Pipeline.
4
Record decisions in the platform
Capture overrides and outcomes where the platform can see them, so
calibration learns from real results and the post-hire loop (coming soon)
can build on them.
5
Review outcomes and recalibrate (coming soon)
Post-hire outcomes will confirm whether scoring still reflects success, and
feed recalibration through change control when it does not. See Outcome
Intelligence for what’s shipping and when.
Why it matters
Reproducible scoring means the same inputs always produce the same score. If configuration is messy or usage is inconsistent, that guarantee still holds, but it faithfully reproduces bad inputs. Clean data is what makes reproducibility valuable.FAQs
Does poor data change the score silently?
Does poor data change the score silently?
No. Scoring stays reproducible and auditable. The risk is that consistent scoring reflects inconsistent inputs, which good data hygiene prevents.
How does this connect to calibration?
How does this connect to calibration?
Calibration compounds from real outcomes. Clean, consistent data is what lets it sharpen rather than drift. See Calibration.
Related
Calibration
How scoring stays reproducible as evaluations run.
Set Scoring Weights
Confirm the rubric and weights behind each score.
View and Manage Candidates
Keeping candidate records accurate.