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Scientific references

The published record behind every number on this surface. Scale-level citations live on the per-scale pages; this page carries the program-level record.

NIST AI 800-3#

NIST AI 800-3, Expanding the AI Evaluation Toolbox with Statistical Models (February 2026), names the interval-scale property AIM is built on. Verbatim:

the intervals between LLM capabilities θ on the latent scale have meaning independent of the difficulties of the specific tasks selected into the benchmark. Raw benchmark scores do not have this property.

How AIM maps to the approach the report highlights as a promising foundation is on the What NIST highlights page of the main site.

Published method#

The measurement method is published, not proprietary black box:

  • Barney, M.F. (2019). The Reciprocal Roles of Artificial Intelligence and Industrial-Organizational Psychology. In R.N. Landers (Ed.), Cambridge Handbook of Technology and Employee Behavior (pp. 38-56). Cambridge University Press. doi.org/10.1017/9781108649636.004
  • Barney, M. and Barney, F. (2024). Transdisciplinary measurement through AI: Hybrid metrology and psychometrics powered by large language models. Chapter 3 in W.P. Fisher Jr. and L. Pendrill (Eds.), Models, Measurement, and Metrology Extending the SI: Trust and Quality Assured Knowledge Infrastructures (pp. 103-132). De Gruyter Oldenbourg. doi.org/10.1515/9783111036496-003. Open access: https://library.oapen.org/bitstream/handle/20.500.12657/94377/1/9783111036496.pdf#page=114
  • Barney, M.F. and Fisher, W. P., Jr. (2017). Avoiding AI Armageddon with Metrologically-Oriented Psychometrics. 18th International Congress of Metrology. doi.org/10.1051/metrology/201709005
  • Barney, M.F. and Fisher, W. P., Jr. (2016). Adaptive Measurement and Assessment. Annual Review of Organizational Psychology and Organizational Behavior, 3, 469-490. doi.org/10.1146/annurev-orgpsych-041015-062329
  • Barney, M., Wind, S., and Krishna, V. (2026). Using large language models to evaluate ethical persuasion text: A measurement modeling approach. IJATE, 13(1), 224-247. doi.org/10.21449/ijate.1788563
  • Barney, M.F. (2010). Inverted Computer-Adaptive Rasch Measurement: Prospects for Virtual and Actual Reality. IACAT, Arnhem. iacat.org

OSF registrations#

The calibration program is pre-registered on the Open Science Framework, with gates and verdicts frozen before data collection:

  • Calibration-validity program: OSF project 3xf6r, registration j95ef (public). osf.io/j95ef
  • Current program, human and AI performance across four work domains on one ruler: OSF registration fh8yd (private until the deposit is public)

Traceability framework#

Construct specification follows the Mari and Wilson metrology-psychometric hexagon framework: every construct specified from measurand to public value. A co-authored paper on MASEMS (Metrological Architecture for the Social, Educational, and Medical Sciences; Thornton, Barney and Fisher) is in preparation.

How to cite a number from AIM#

Cite the band location, the standard error stated at that location, the scale name, the certification tier, and the calibration vintage. A number without its SE and vintage is not an AIM number and should not be attributed to AIM.