Recent MLLMs have shown promise in scene-text-aware video understanding, yet their ability to understand scene text from the wearer’s perspective in AI-glasses videos remains underexplored. AI-glasses videos are passively captured without a camera-frame preview, limiting the wearer’s ability to adjust framing during recording. As a result, relevant text may be unstable, and the recorded view may not fully reflect the wearer’s attention. The challenge is thus not merely to read visible text, but to identify and read the right text given the wearer’s position, viewpoint trajectory, and goal. To systematically evaluate this capability, we introduce **WearerText**, a benchmark for **Wearer**-Centered Scene **Text** Understanding, comprising 1,101 real-world AI-glasses videos and 15,336 QA pairs spanning 13 tasks. These tasks are organized into a three-level diagnostic hierarchy: *L1 Text Perception*, *L2 Contextual Understanding*, and *L3 Wearer-Grounded Reasoning*, targeting unstable text perception, wearer-view alignment, and goal-oriented text reasoning, respectively. To ensure reliable construction and evaluation, we develop **MAH-V**, a multi-agent hybrid verification pipeline for QA construction, and propose **VerEval**, a Verified-evidence metric for open-ended Evaluation. Benchmarking 20 recent MLLMs shows that current models still struggle with wearer-centered scene text understanding, particularly on tasks requiring wearer-view alignment and goal-oriented text reasoning.

Off-the-shelf models are widely used by computational social science researchers to measure properties of text, such as sentiment. However, without access to source data it is difficult to account for domain shift, which represents a threat to validity. Here, we treat domain adaptation as a modular process that involves separate model producers and model consumers, and show how they can independently cooperate to facilitate more accurate measurements of text. We introduce two lightweight techniques for this scenario, and demonstrate that they reliably increase out-of-domain accuracy on four multi-domain text classification datasets when used with linear and contextual embedding models. We conclude with recommendations for model producers and consumers, and release models and replication code to accompany this paper.