Position-Adjusted Word Count (PAWC)

Position-Adjusted Word Count, or PAWC, is the metric the Princeton and IIT Delhi GEO paper (arXiv 2311.09735) uses to score a source's visibility within an AI-generated answer, weighting how many of that source's words appear in the response and where they fall, with earlier-appearing content weighted more heavily.

Why it matters

PAWC is the paper's core measurement tool, and understanding it clarifies what GEO tactics are actually optimizing for: not simply whether a source gets mentioned, but how much of the answer's substance, and how prominent a position within it, that source's content actually earns.

Related terms

• GEO-bench

  GEO-bench is the benchmark the Princeton and IIT Delhi GEO paper built to test and compare optimization tactics, consisting of 10,000 queries spanning 8 different domains, used as the standardized dataset every tactic in the study, from statistics addition to keyword stuffing, was measured against.

• Subjective Impression

  Subjective impression is the Princeton GEO paper's human-evaluated score for how visible and prominent a source feels within an AI-generated answer, collected by having human raters read the answer and judge a source's presence directly, as a complement to the automated PAWC metric.

• Answer Position

  Answer position is where a brand's mention or citation falls within an AI-generated response, whether it's named first or last, and where it ranks within the list of cited sources shown alongside the answer.