will.anderson
afc92f4e33
The soul's curiosity auto-term extractor takes the first word of a top-WM
node label. It has no term-quality scoring, so three prior self-reviews each
bolted on another hand-curated blocklist (genre words 07-23, quoted titles
07-25, stopwords 07-30). Every one was written reactively, after a flood was
already observed. A list can only contain floods that already happened.
Two were in flight and unfixed when this review ran:
"<!--" label df 220 -> 252 nodes activated
"SELF" label df 175 -> 541 nodes activated (list has "Self" Title-case;
str_eq is case-sensitive, so the uppercase token sailed through)
engram_label_df(term) counts nodes whose label contains term. Low-specificity
tokens are corpus-frequent by definition, so this catches the flood class
prospectively and tracks the corpus as the world-ingestor changes it. This is
Sparck Jones (1972), which introduced IDF under the name 'term specificity';
automatic stopword compilation from it is the textbook application.
NOT a replacement for the stopword list -- verified against all 86 listed
terms, not assumed. Catches 13 (Will:306, Self:175, Over:116, Knowledge:112),
misses 73 (Whose:0, Would:0, Could:0, This:9). Labels are terse titles, so
English function words are genuinely rare in them. The gates cover disjoint
failure modes; both are required.
Policy lives in awareness.el, not here: the runtime measures, the soul decides.