Score breakdown

Scaling Laws for Neural Language Models

paper-0124 · paper · 2020

Jared Kaplan et al.

Loss as a power law of compute, data, parameters; the industry's planning document.

Academic, score -0.1966

MetricStatusValueNorm.WeightContributionSourceConfidenceProvenance
citation_countpresent1512.00.0068020.50.003401OpenAlexhighlink
library_holdingsmissingrecorded as missing, penalized by rule, never imputed−0.1recorded as missing; penalized by rule, never imputed
readership_persistencepresent8.00.50.050.025OpenAlexmediumlink
syllabus_adoptionsmissingrecorded as missing, penalized by rule, never imputed−0.125recorded as missing; penalized by rule, never imputed

Broad Influence, score 0.0014

MetricStatusValueNorm.WeightContributionSourceConfidenceProvenance
citation_countpresent1512.00.0068020.20.00136OpenAlexhighlink
library_holdingsmissingrecorded as missing, penalized by rule, never imputed−0.125recorded as missing; penalized by rule, never imputed
readership_persistencepresent8.00.50.40.2OpenAlexmediumlink
syllabus_adoptionsmissingrecorded as missing, penalized by rule, never imputed−0.075recorded as missing; penalized by rule, never imputed

Governance Practitioner, score -0.2733

MetricStatusValueNorm.WeightContributionSourceConfidenceProvenance
citation_countpresent1512.00.0068020.250.0017OpenAlexhighlink
library_holdingsmissingrecorded as missing, penalized by rule, never imputed−0.15recorded as missing; penalized by rule, never imputed
readership_persistencepresent8.00.50.10.05OpenAlexmediumlink
syllabus_adoptionsmissingrecorded as missing, penalized by rule, never imputed−0.175recorded as missing; penalized by rule, never imputed

A rank is not a verdict on intrinsic worth. It is a transparent output of declared evidence, weights, and missing-data rules at a specific release date.

Disagree with this rank or a number? Challenge it with your evidence. Every challenge gets a public identifier and a published resolution.