How the scoring works
The calculator translates evidence into a transparent weighting so you can see which self-petition track appears stronger. EB-1A factors emphasize sustained acclaim and one-time major achievements. EB-2 NIW factors emphasize national importance, practicality, and whether your impact is sufficiently broad to justify a waiver of the usual job-offer and labor-certification requirements.
EB-1A score tiers emphasize the three-prong framework articulated in Kazarian: (1) qualifying criteria met; (2) sustained acclaim; and (3) overall final merits. EB-2 NIW follows Dhanasar: (1) substantial merit and national importance; (2) well-positioned to advance; and (3) benefit to the U.S. outweighs job-offer requirements.
The simplified weighting uses a logarithmic smooth for counts to avoid runaway citation and publication inflation. Citations are normalized with a natural log, while binary contributions or roles add discrete points. The national interest intensity is scaled between 0 and 10 for clarity.
The normalized research signal is computed as:
where C is citations and P is publications. Awards, judging, leadership, and media add additive boosts. EB-1A multiplies the research signal by 1.2 to reflect the “sustained acclaim” expectation, while EB-2 NIW multiplies the national benefit inputs by 1.3 because the waiver analysis hinges on public benefit rather than acclaim alone.
The final EB-1A score is:
with A=awards (capped at 3), J=judging/2, L=leadership, M=memberships/2, O=original contributions flag, and T=media/3. The EB-2 NIW score is:
where B=benefit plan clarity/10, Y=policy alignment/10, N=letters/5, and H=job offer reliance/10. The formulas are heuristic and meant to guide strategy, not replace legal advice.
Worked example
Consider an applied AI scientist in climate modeling with 20 publications, 600 citations, two national awards, six judging engagements, two leading roles, three media pieces, and one commercialized patent. They have seven persuasive letters from independent experts, an 8/10 national benefit plan, 9/10 policy alignment, and low dependence on a job offer (2/10). EB-1A inflates research prestige and awards, while EB-2 NIW rewards benefit clarity and national importance.
Plugging the values, the research signal is R = ln(600+1)/5 + 20/20 ≈ 1.38 + 1 = 2.38. EB-1A score becomes 1.2×2.38 + 4(2 + 3 + 2 + 0.5) + 6(1) + 5(1) ≈ 2.86 + 30 + 6 + 5 = 43.86. EB-2 NIW score becomes 1.1×2.38 + 3(1) + 2(2) + 0.8(0.8) + 0.6(0.9) + 4(1.4) – 0.5(0.2) ≈ 2.62 + 3 + 4 + 0.64 + 0.54 + 5.6 – 0.1 = 16.3. The EB-1A path looks more favorable here because the profile is award-heavy and has media visibility.
The example also shows how job-offer dependence (H) can suppress NIW strength. An entrepreneur with no employer ties scores better on NIW because the waiver is meaningful when labor-certification is impractical.
Limitations and assumptions
These scores are heuristic and deliberately conservative. They do not incorporate peer reputation nuances, RFE trends in a particular service center, country quotas, or premium processing availability. The weighting compresses extreme citation outliers and assumes evidence quality is consistent. Always consult an immigration attorney for individualized advice.
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