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.
Each script maps to one research question in the paper.
RQ1 - does the metric separate simplified from original, and does the confidence weighting help?
python -m experiments.rq1_validate --dataset deplain_web --scores scores/deplain_web.jsonl
Same for the other corpora, swapping --dataset and the bundle path:
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):
python -m experiments.rq2_convergent --scores-dir scores
Single-corpus run (any one dataset, e.g. apa_lha):
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
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.
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:
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.