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📊 Planned Analysis

To move beyond simple "behavioral compliance," we will employ computational modeling to understand why the intervention works.

Primary Outcome Adoption Rate

modeled via Mixed-Effects Logistic Regression

Mechanism Prosocial Δ

Pre/Post test difference (Interaction Effect)

Moderator Autonomy

Correlation between "Perceived Fairness" and "Latency"

The Hypothesis: High adoption rates paired with high autonomy scores will predict lasting behavioral change (Social Learning) rather than just suppression.

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📚 Theoretical Underpinnings

This prototype explores AI-Mediated Communication (AI-MC). The goal is to find a balance between teaching youth towards positivity in social spaces while preserving user autonomy.

Research Questions: We are measuring acceptance rates and prosociality scores to determine if exposure to these interventions truly makes people more prosocial over time, or if it simply masks intent.

See also: