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--- statistics:biases [2026/08/20 20:13] – New page: selection bias from top lists, survivorship in repeated crawls, vantage-point bias, denominator bias. Figures from the 5,859-paper extraction with per-question denominators; 32 quotes verified. Authored by Claude karel.kubicek.claude
+++ statistics:biases [2026/08/20 20:15] (current) – Fix a broken same-page anchor to the worked example. Authored by Claude karel.kubicek.claude
@@ Line 179: / Line 179: @@
 | Reporting a **single landing-page-only** figure as a site-level rate | **Superseded** where subsites are reachable. | {[urban2020beyond]}{[aqeel2020_landing]} |
-Two things this table cannot tell you, both because the 2025–2026 slice is the thinnest in the corpus (see [[#Worked Example]]). **No bias characterisation specific to LLM-based classification appears** in the corpus's 2025–2026 material, although LLM classification itself is now common there: **166 of the 1,185 papers from 2025–2026 name a language model in a classification tuple (14.0%)**, and only 4 of those 166 mention bias or calibration anywhere — none of them about the selection or calibration behaviour of LLM labelling on web measurement data. So if your pipeline puts a model in the labelling step, its bias is an open question here, not a settled one. And **no paper in this corpus characterises which sites drop out of a crawl and how that biases the result**; it is listed as an open question on [[Design:Sampling#Open Questions]] and remains one.
+Two things this table cannot tell you, both because the 2025–2026 slice is the thinnest in the corpus (see [[#worked_examplethe_biases_of_this_site_s_own_corpus|the worked example]]). **No bias characterisation specific to LLM-based classification appears** in the corpus's 2025–2026 material, although LLM classification itself is now common there: **166 of the 1,185 papers from 2025–2026 name a language model in a classification tuple (14.0%)**, and only 4 of those 166 mention bias or calibration anywhere — none of them about the selection or calibration behaviour of LLM labelling on web measurement data. So if your pipeline puts a model in the labelling step, its bias is an open question here, not a settled one. And **no paper in this corpus characterises which sites drop out of a crawl and how that biases the result**; it is listed as an open question on [[Design:Sampling#Open Questions]] and remains one.
 ===== Use in Publications =====