Selecting compact training sets from heterogeneous text sources requires balancing target relevance, label reliability, and coverage. For small classifiers, discarding unfamiliar but useful texts can be as harmful as retaining annotation errors. Gradient-based selection estimates immediate target utility, whereas confidence and coverage scores capture complementary properties. Combining them with fixed weights leaves the utility sacrificed for this evidence uncontrolled across candidate pools. We propose Data Value Contribution (\DVC), which gates lexical, hidden-layer, and prediction-level evidence by neighborhood label support and calibrates its influence under a local utility budget; exact MLP gradient contractions and a monotone scoring path make the budget directly verifiable. At 20\% source retention, DVC achieves 75.4 mean macro F1 across eight classification and domain-transfer tasks, exceeding the strongest baseline under shared final training by 0.7 points and random selection by 4.3; an equal-budget tuning study confirms a 0.6-point gain over calibrated quality-only selection.

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