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Field
Intervention (Hidden)
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Before
The AI facilitator combines a multi-turn chat with a separate proposal-generation model that produces a structured proposal users can edit before submission. The procedural and open-ended modes differ only in the chat prompt’s conduct block: the former follows problem, context, proposal, synthesis and confirmation phases; the latter has no fixed sequence. Other prompt components and the proposal-generation step are identical across arms. The self-authored route uses the same proposal fields. Route compliance is observed but not enforced; switching requires abandoning the current draft or conversation. Tagged viewers see “Creata con AIdea” or “Scritta in proprio”; untagged viewers see neither.
A separate experiment during the October 2–4 final vote on twelve policy pillars assigns eligible users to no advice, comfort advice or outside-comfort-zone advice. Recommendations are presented as coming from AI and drawn uniformly from pillars linked to the user’s prior platform activity or from the remaining pillars, respectively. Assignment is stratified by prior activity, with rerandomization to balance pillar-set membership. Full prompts, eligibility rules and implementation details are provided in the updated pre-analysis plan.
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After
The AI facilitator combines a multi-turn chat with a separate proposal-generation model that produces a structured proposal users can edit before submission. The procedural and open-ended modes differ only in the chat prompt’s conduct block: the former follows problem, context, proposal, synthesis and confirmation phases; the latter has no fixed sequence. Other prompt components and the proposal-generation step are identical across arms. The self-authored route uses the same proposal fields. Route compliance is observed but not enforced; switching requires abandoning the current draft or conversation. Tagged viewers see “Creata con AIdea” or “Scritta in proprio”; untagged viewers see neither.
A separate experiment during the October 2–5 final vote on twelve policy pillars assigns eligible users to no advice, comfort advice or outside-comfort-zone advice. Recommendations are presented as coming from AI and drawn uniformly from pillars linked to the user’s prior platform activity or from the remaining pillars, respectively. Assignment is stratified by prior activity, with rerandomization to balance pillar-set membership. Full prompts, eligibility rules and implementation details are provided in the updated pre-analysis plan.
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