Multilingual language modeling is often evaluated through broad aggregate scores, yet language-specific failures can remain hidden, especially for morphologically rich and underrepresented languages. We present NEDOQwen, a Turkish-centric 0.824B decoder-only small language model research artifact, and use it to study a low-cost diagnosis-and-repair workflow for Turkish evaluation. Internal Turkish instruction diagnostics improve after supervised fine-tuning, but external Turkish and Turkic multiple-choice benchmarks reveal weak academic knowledge and severe answer-label bias. A compact benchmark-safe repair stage yields a small improvement on TurkishMMLU-sub, from 18.11% to 21.00%, and a larger improvement on TUMLU-mini Turkish, from 21.33% to 32.22%, while an answer-label-only ablation reaches 19.56% and 27.00%. We further analyze tokenizer coverage, internal-vs-external evaluation mismatch, prediction-label distributions, paired uncertainty, and compute cost. The contribution is not a state-of-the-art Turkish model, but an auditable case study showing how small language-specific models can expose and partially repair multilingual evaluation failures while remaining unreliable.