The same CRS for every viewer
A 0–1000 score derived from the same eligible evidence and formula version. A recruiter cannot privately rewrite someone’s canonical score.
Career Rank Score / CRS v4
One score.
Many lenses.
CRS turns eligible career evidence into one transparent, viewer-independent score. Open Algorithm lets you decide how to filter and sort that evidence—without changing the person’s CRS.
Deterministic, versioned, explainable. Decision support, never an automatic hiring decision.
Score available
crs-v4
842/ 1000
The separation that matters
A measurement is not a decision.
CRS, the evidence beneath it, and your search preferences are three distinct things. Keeping them separate makes the system easier to understand, challenge, and improve.
Coverage stays visible
Confidence, verification coverage, component breakdowns, and freshness help viewers understand what supports the score—and what is missing.
Your policy, clearly labeled
Open Algorithm filters and sorts for your use case. The policy is deterministic, saved by scope and version, and shown beside its result.
How the score is built
Career evidence, weighted in the open.
The current CRS formula combines submitted four-dimensional reviews with dated work, completed education, and skills or certificates. Missing components are renormalized rather than treated as zero.
4D reviews
Eligible submitted reviews across rating, integrity, intelligence, and energy. No review means unrated—not automatically poor.
Dated work
Work-experience records with usable dates. Tenure is evidence context, not account age or a proxy for a protected trait.
Education
The strongest completed degree level supplies most of this component. When a supported grading scale is usable, normalized GPA supplies the remainder; a missing GPA is neutral. School prestige is not scored.
Skills & certificates
Declared skill and certificate evidence, with verification coverage exposed separately from the canonical score.
Current published formula: crs-v4. Education uses degree level for 80% of its component and normalized GPA for 20% when a 4, 5, 10, 20, or 100-point scale is usable. The percentage weights above apply when all components are available. Formula versions and score snapshots preserve reproducibility.
Open Algorithm / Build your own algorithm
A filter you can explain.
“Build your own algorithm” sounds complex. In practice, it is a saved set of filters, thresholds, weights, and a sort metric. It does not need a language model and it does not create a second CRS.
Choose a preset lens
Watch the same fictional profiles reorder while their canonical scores stay fixed.
Canonical CRS order
Same viewer-independent 0–1000 score for everyone.
Profile Aillustrative data
842canonical CRS
86%verified
105.3per verified yr
Profile Billustrative data
816canonical CRS
64%verified
136.0per verified yr
Profile Cillustrative data
788canonical CRS
93%verified
197.0per verified yr
Profile Dillustrative data
752canonical CRS
78%verified
62.7per verified yr
Illustration only; no real candidate data. “Score per verified year” is one optional ordering lens, not a claim that a higher rate means a better person or hire.
What a saved policy can contain
Useful controls, with provenance.
A lens can apply to you, a company, or a job. Server-side authorization controls who can create or change it, and immutable versions preserve what was used.
Use all eligible evidence or a verified-only view. Verification is a separate breakdown, never a hidden score multiplier.
Minimum or maximum CRS, confidence, verification coverage, and verified work-year thresholds.
Canonical score, explicit weighted dimensions, or a derived score-per-verified-year ordering.
See original CRS, derived value, confidence, policy version, and explanation for each ranked result.
Human-decision boundary
Signal in. Judgment stays human.
CRS can help people inspect and organize evidence. It cannot establish suitability for a role by itself, replace due diligence, or make an employment decision.
Designed to do
- Show the evidence components and formula version behind a score
- Expose confidence, verification coverage, freshness, and missing inputs
- Apply reversible filters and explain why a result appears
- Let a person correct source profile data through Sorcrr controls
Not designed to do
- Use age, account age, salary, referrals, or institution prestige in CRS
- Use protected traits as Open Algorithm filters or weights
- Automatically reject, hire, fire, or otherwise decide for a human
- Hide a viewer’s ranking policy inside the canonical score
Questions worth asking
CRS, without the black box.
For operational details, authenticated Sorcrr users and internal agents can use the canonical wiki article linked below.
Is CRS an AI score?
No. CRS is calculated by a deterministic, versioned formula. SAI may explain a score or help a user configure a lens, but the score and ranking tool do not require a language model or AI credits.
Can a hiring team change my CRS?
No. A viewer can save a ranking policy for their own authorized scope, but that policy does not rewrite your canonical CRS. The original score remains visible alongside any derived ranking value.
Why might a score be unavailable or stale?
CRS exposes availability states such as available, unrated, stale, or unavailable. A profile may lack enough eligible evidence, have evidence awaiting recomputation, or be outside an authorized view.
What should an employer do with a ranking?
Review the evidence, job requirements, accommodations, and relevant context; keep a qualified human in control; and meet any notice, audit, assessment, or appeal duties that apply in the employer’s jurisdiction.
Transparent by construction
Bring your lens. Keep the evidence visible.
Use CRS to understand a career evidence snapshot, then use Open Algorithm to make your own priorities explicit—not to hide them.