Engagement detection in meetings has focused mostly on speakers, who produce explicit prosodic and acoustic cues. The engagement of non-speaking participants is harder to read because it surfaces only in nonverbal behaviour. We predict how engaged a silent listener is, on a three-level scale (Engaged, Intermittent, Disengaged), in five-second windows of remote Zoom meetings from the GEstures and Head Movements in Language (GEHM) corpus. Each window is described by three feature families: the listener’s own nonverbal behaviour, the listener’s behavioural alignment with the others present, including the active speaker, and that speaker’s prosody. Interpersonal coordination is an established correlate of rapport and involvement. We ask the narrower question of whether a compact window-level alignment measure adds predictive value over listener-only features when confident labels are scarce. Under leakage-free cross-validation, listener-speaker alignment is the most useful addition we test, and the speaker’s terminal pitch slope is an interpretable correlate of listener engagement. The gains are modest and their bootstrap intervals include zero. Unsupervised analysis finds no engagement structure. Two findings are firmer: the task is effectively binary, actively engaged versus not, bounded by the scarcity of confident disengaged labels, and the pitch-slope correlate holds despite the small sample.

Evaluation of cultural value alignment in Large Language Models (LLMs) typically relies on direct prompting with survey-style questions, which often elicit neutral or safety-aligned responses and fail to capture the model’s underlying cultural orientation. To overcome these challenges, we propose a framework for probing and steering latent cultural representations in LLMs along the two Inglehart–Welzel axes of the World Values Survey (WVS). By translating social value questions into scenario-based dilemmas, we extract token-level probabilities to measure implicit values and apply activation steering, optionally combined with country-conditioned prompting, to shift model behavior without retraining. Across three 4B-open-weight LLMs, four countries and three social domains, we find substantial variation in steerability. Importantly, we identify latent entanglement, where interventions along one Inglehart–Welzel axis induce a shift along the other. This entanglement is consistent with correlations present in human WVS data and persists across our experimental settings. Our work identifies empirical constraints on cultural alignment in LLMs.