# Reproduce

Every metric table in the paper comes from the released, text-free score bundles in `scores/`. No closed scorer and no restricted corpus is needed. Install the analysis stack (`pip install -r requirements.txt`) and run the commands below from the repo root. To see the text behind a score, see [`DATASETS.md`](./DATASETS.md).

Each script maps to one research question in the paper.

## RQ1 - does the metric separate simplified from original, and does the confidence weighting help?

```bash
python -m experiments.rq1_validate --dataset deplain_web --scores scores/deplain_web.jsonl
```

Same for the other corpora, swapping `--dataset` and the bundle path:

```bash
python -m experiments.rq1_validate --dataset toborek     --scores scores/toborek.jsonl
python -m experiments.rq1_validate --dataset apa_lha     --scores scores/apa_lha.jsonl
python -m experiments.rq1_validate --dataset deplain_apa --scores scores/deplain_apa.jsonl
```

Writes `results/validation_stats_<dataset>_mw100.json` and prints the discrimination and ablation report to stdout.

## RQ2 - convergent validity vs. standard readability indices

Canonical two-corpus run (toborek + deplain_web, matches the paper's figure):

```bash
python -m experiments.rq2_convergent --scores-dir scores
```

Single-corpus run (any one dataset, e.g. apa_lha):

```bash
python -m experiments.rq2_convergent --dataset apa_lha --scores-dir scores
```

Writes `results/rq2_convergent.json` (plus a `.png` scatter), or `results/rq2_convergent_<dataset>.json`.

## RQ3 - graded monotonicity across CEFR bands

```bash
python -m experiments.rq3_graded --scores scores/apa_lha.jsonl
```

Writes `results/rq3_graded.json`.

## RQ4 - competitor comparison and equivalence test

Reads the `scores/competitors.jsonl` bundle (one row per (dataset, item, system): source/human/KLAR plus competitor models). Defaults to `--datasets toborek,deplain_web` (n=514), matching the paper's as-submitted table.

```bash
python -m experiments.rq4_competitors
python -m experiments.rq4_tost
```

Writes `results/rq4_competitors/summary.json` and `results/rq4_tost.json`, and prints the head-to-head (composite plus independent Flesch) and TOST equivalence reports.

For the pooled n=997 variant (DEplain-APA pooled in after submission), pass the same `--datasets` to both, since `rq4_tost.py`'s self-test reconciles against `rq4_competitors`' `summary.json`:

```bash
python -m experiments.rq4_competitors --datasets toborek,deplain_web,deplain_apa
python -m experiments.rq4_tost --datasets toborek,deplain_web,deplain_apa
```

Competitor subsets are selectable independently of dataset scope, e.g. `--systems capito`.