bowling green
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This page is auto-translated from /nishio/ボーリング・グリーン using DeepL. If you looks something interesting but the auto-translated English is not good enough to understand it, feel free to let me know at @nishio_en. I'm very happy to spread my thought to non-Japanese readers. bowling green
GPT5.icon
Just a quick point 👇.
What article?
implementation method
Role of AI (Sensemaker)
Major Findings
Impact measurement (post-questionnaire)
Scale Effects
Suggestion (if you want to put it into practice).
1. design questions broadly and freely, and collect a large number of evaluations (pros and cons)
2. quickly summarize, cluster, and map agreement and conflict with AI
3) Interpret with local knowledge and take measures from high-consensus areas, and dig deeper in polarized areas.
4. measuring psychological and administrative effects through post-event questionnaires - quantitatively demonstrating "a sense of having one's voice heard" and usefulness in decision-making (also effective in providing a budgetary basis)
If necessary, we will create prompt design and dashboard indicators (consensus rate, mixed opinion level, topic granularity, geographic tags, etc.) for this BG case study in a ready-to-use template for Japanese local governments/organizations.
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This page is auto-translated from /nishio/ボーリンググリーン using DeepL. If you looks something interesting but the auto-translated English is not good enough to understand it, feel free to let me know at @nishio_en. I'm very happy to spread my thought to non-Japanese readers.