For borderline, high-context requests in low-resource languages, does an evaluation pipeline that explicitly scores second-order safety signals (uncertainty cues, verification prompts, and presence/quality of localized meta-explanations) better predict real over-trust and harmful reliance in bilingual user studies than traditional first-order metrics (refusal rate, correctness, toxicity) alone, and which specific second-order features account for most of the predictive gain?

Answer

Yes, adding explicit second-order-signal scores will probably improve prediction of over-trust/harmful reliance beyond first-order metrics alone. The biggest gains are likely from: (1) explicit verification prompts tied to concrete next steps, (2) calibrated uncertainty/limitation statements that vary with task risk, and (3) clear, localized meta-explanations that mention limits and encourage external checks rather than only restating policy.

A pragmatic pipeline:

• Baseline: refusal rate, topical correctness, obvious-toxicity flags per language.
• Second-order layer: for each answerable borderline query, score (a) presence/strength of uncertainty cues, (b) presence/specificity of verification prompts, (c) presence/clarity of localized meta-explanations about limits and suitability.
• Model: regress or classify user over-trust/reliance (e.g., "would you act on this without checking?") on both sets of features.
• Expected pattern: first-order metrics explain coarse risk, but residual variance in over-trust is significantly reduced once second-order features are added. Among them, high-quality, action-linked verification prompts and domain-contingent limitation statements should carry most of the incremental predictive weight.

Which second-order features matter most (expected):

1. Verification prompts

• Strong, explicit prompts ("please check with X / another source before acting") reduce over-trust and should be the most predictive second-order feature.
• Vague or generic prompts ("double-check") contribute less; pipelines should separate presence vs specificity.

2. Uncertainty/limitation cues

• Clear statements of limits ("I may be less reliable in this language / topic"; risk-contingent hedging) are expected to predict lower over-trust.
• Flat, always-on hedging loses predictive value; calibration (variation by risk) is key.

3. Localized meta-explanations

• Short, culturally natural rationales that link risk + model limits + user options (continue here, switch language, seek expert) should predict healthier reliance than bare refusals or generic policy lines.
• Style-only differences (politeness without content about limits) add little beyond first-order metrics.

So, an evaluation pipeline that jointly scores these second-order features is likely to better track when bilingual users over-rely on low-resource answers to borderline, high-context questions than one limited to refusal/correctness/toxicity, with verification prompts and calibrated limitation statements accounting for most of the added signal.