docs/methodology.md

Methodology: CodeMixCheck v2.2.1

Task design

CodeMixCheck treats fact verification as claim-level veracity classification with an explicit evidence state. Each synthetic claim (proposition) is assigned a veracity state in a simulated world, and is then rendered through different frames, post formats and levels of noise. This separates:

• what is claimed (the proposition, with its veracity),
• how it is framed (forwarded message / official attribution / hearsay / report),
• how noisy the surface text is.

All four labels occur under every frame family. This is intended to stop a classifier from equating hearsay framing with falsehood.

evidence_explanation is a short justification (mostly templated) that states the simulated evidence. No retrievable evidence corpus is included. RAG experiments therefore need an external or separately built evidence store, and that store must stay consistent with the simulated world.

AI-assisted generation

component	what is recorded	what is not recorded
template rows	provenance = template_generated, batch, frame, format, noise	the generating script, model or chat assistant used to write templates/entity pools, prompts, dates
curated rows	human_curated_llm_assisted, news_sourced_and_llm_assisted	which LLM, which prompts, which sources, how much text the LLM wrote versus the author
silver sentiment	model_annotated_v1.3	the model behind "v1.3"

The project used Claude and ChatGPT during dataset construction. Because no per-row or per-batch attribution was stored, this release labels the generator not_recorded rather than assigning a model to any record.

Splitting

• Fact verification: train 5,337 / validation 662 / synthetic test 661 / curated test 344. The synthetic splits are drawn from the same generator. The curated test is held out by origin.
• Splits are grouped by content_group and template_group. Neither spans two splits.
• v2.1 removed template-family leakage by moving 22 rows.
• The curated test carries cv_fold (0–4; folds of 69/69/69/69/68 rows) for cross-validated evaluation.

Recommended evaluation protocol

1. Report macro-F1 and per-class F1 separately for test_synthetic and (when available) test_curated.
2. For test_curated, also report results per eval_subset, because the label distributions differ sharply (hearsay = 100% Unverifiable; health/lifestyle = 90% True).
3. Report a strict test_synthetic score that excludes rows flagged D01/D02 (cross-split same-claim or near-duplicate rows).
4. Do not tune on test_*. Sentiment and abuse have very small validation sets, so expect high variance and report confidence intervals.
5. Do not train on silver_unverified and then describe the result as trained on gold.