When Audit Quality Fails to Predict Downstream Utility: A Counterfactual Study of Synthetic-Data Selectors for Low-Resource African NLP

Quality-aware synthetic-data selection rests on a proxy: examples that an LLM judge rates as good should also help a downstream model learn. In a controlled replay in low-resource African-language classification, we show that this proxy breaks. Across four languages (Amharic, Hausa, Swahili, Yoruba), two classification tasks (MasakhaNEWS, AfriSenti), and five matched-budget selectors, audit rankings and downstream rankings diverge. Within each cell, the Spearman between judged label correctness and Macro-F1 across selectors has mean ρ=0.04 (median 0.00), showing that the mismatch is not an aggregation artifact. method-V2, our counterfactual audit framework, produces the cleanest selected pool on three audit channels at once: highest judged label correctness (0.904 vs. 0.767 for naive, a 17.9% relative gain), lowest shortcut score, and a hard-reject rate of 0.162 vs. 0.486 for naive. AlpaGasus nevertheless leads downstream Macro-F1 (0.202 vs. 0.163 for method-V2), and the inversion persists on the five non-degenerate cells. The lesson is methodological: in this controlled setting, audit quality is a property of the selected pool, not a guarantee of downstream utility. Synthetic-data evaluation should therefore report audit and downstream metrics on the same retained sets. We release the audit tables, per-selector retained pools, and a claim ledger that links every reported number to its source row.