# 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.