README.md

metadata

license: apache-2.0
language:
  - en
tags:
  - anima
  - earned-operator-supply
  - recombination
  - sentiment
  - negation
task_categories:
  - text-classification

anima-earned-datascale — H_9968 natural-corpus operator supply vs DATA scale

The top-rung fixed-length in-band corpus for anima hypothesis H_9968: does the natural-corpus supply of a transferable recombination operator (negation flips sentiment polarity independent of the stem) grow with data scale, with sentence length held fixed?

This is the p9 "one unopened cell" screener corpus, measured by the certified anima-py evaluate --earned instrument (corpus-statistics, never touches a trained model = the SUPPLY upper bound). DIRECTIONAL screener, never cemented.

Provenance

• Source: fancyzhx/amazon_polarity (3.6M human-labeled English customer reviews; star rating is the label, OUTSIDE the token stream).
• Filter (row-selection only, corpus-prep — NOT an estimator change): keep rows whose whitespace token count is in the frozen band 20–50 tokens (overlaps SST-2's long tercile, where the matched-length +0.007 baseline was read). This holds length — the dominant driver of the EN–KO gap (H_9951) — constant across every future size rung.
• Result: 1,230,238 in-band rows from 3,600,000 train rows.

Schema (--earned format: text<TAB>B<TAB>T)

• text — the review body (tabs/newlines flattened to spaces).
• B — free-negation bit: 1 if the text contains a free/pre-posed negator from the closed set {not, no, never, none, nothing, nobody, nowhere, neither, nor, without} or an n't clitic, else 0. (Same closed set as the EN FREE arm, build_morph_split.py.) B=1 rate = 0.5028.
• T — Amazon star label binarized (1–2 → 0 negative, 4–5 → 1 positive). T=1 rate = 0.5462.

Integrity

• amazon_inband_full.tsv — sha256 ed326109ac05eaf5307e4d0409d22ead3183392f001f770aac3ebbfcd0ae6ce0, 231,743,573 bytes, 1,230,238 rows. (HF-backup decidability is by sha256, not by name.)
• build_amazon_inband.py — the exact deterministic builder (fetch → band filter → B/T emit).

How it is read (H_9968, decision table frozen before any number)

Run anima-py evaluate --earned amazon_inband_full.tsv. This is the TOP-rung ABORT gate:

• EARNED ≤ +0.03 with all three gates (G-ALIVE / G-PEDESTAL / G-POWER) green → CLOSED: the wall is data-scale-invariant over the whole labeled-natural range.
• EARNED > +0.03 → build the nested size ladder (30k → 100k → 300k → 1M → all) to locate where it climbs.

Baseline: at matched length both English and Korean sit at ~+0.007 nats = 0.13% of the planted XBIND ruler (+5.29653). The claim this corpus can support is the SLOPE over the labeled-natural range; the 10^12 LLM regime is unlabeled and this label-dependent instrument can never reach it.

Card: HYPOTHESES/cards/Hc_H_9968_prereg_datascale_operator_supply.md in the anima repo.