| --- |
| 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`](https://huggingface.co/datasets/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. |
| |
