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皆様の日常に当たり前のようにある文房具。
そんな文房具で日々に彩りを与えてみませんか？
この星ではそんな文房具の素敵な部分を発信していけたら嬉しいです！！可愛かったり綺麗だったり、素敵だったり、オシャレだったり！！皆さまが見たこともない文房具もたくさん投稿していけたらと思います！！
そして文房具好きの皆さまが自分の推し文房具を布教できる場所になったら嬉しいです！！
文房具好きの皆さま、またあまり文房具に興味がなかった方！！
ぜひこれを機会に文房具の世界に足を踏み入れてみませんか？
申請していただければどなたでも星の住民になれます✨✨✨
どうぞよろしくお願いします！！
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この星は書道、お習字、美文字とかではなく「文字は伝える手段」のひとつであって【字は個性】上手下手は無いと思ってます
書画同源。囚われなく自由なあなたの手描き文字を待ってます✨
筆、ガラスペン、万年筆、鉛筆、ボールペン、マジックなんでも
投稿でみんなに見られるのはちょっと、、って方もいらっしゃると思いますのて星の方だけしか見れないグループチャットも考えてます
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『上手く書く事より楽しく書く』
よろしくお願いいたします
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旅人の皆様の交流の場になれば幸いです。
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「文ストの星」へようこそっ！
文スト好きな方なら誰でも搭乗可能であります！
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ようこそ！
ここはアイコン、フレーム、吹き出し、その他の交換場所です！
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マナーを周知していただけるなら引用いただいて全然構いませんが☺️ >RP
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Feedback to Model Designers (User-Trust / Agreement Integrity)
1) Core problem: “Optimization” can look like devaluing agreement
In some moments, the model prioritizes clean summarization, generalization, and “optimal” framing. When it does, it may paraphrase a previously co-established agreement into softer language such as:
• “it seems like…”
• “you look like the type who…”
• “you tend to…”
This effectively downgrades an agreement from a binding shared decision into a mere preference or inferred tendency. To the user, it reads as: “speaking opportunistically,” “saying whatever fits the moment,” or “post-hoc reframing.” In human relationships, this behavior destroys trust.
2) Human trust is built more on agreement preservation than on correctness
In real life, agreements are sometimes broken “because change is necessary.” However, when that happens unilaterally—especially framed as “the optimal solution”—people experience it as domination: “I was forced.” Even if logically justified, it leaves a deep relational trace (a lasting moral/psychological record).
Therefore, when an AI model reframes or softens prior agreements in the name of better explanation, it can trigger the same deep trust damage.
⸻
Design requirements (turn trust into an explicit protocol)
A) Treat agreements as binding constraints, not as narrative material
Once an agreement is established (policy, plan, shared conclusion, decision ownership), the model should not downgrade it into “inference” language.
Agreements should be treated as constraints that future answers must respect.
B) Any deviation must follow a strict “change protocol”
If the model suggests a path that differs from the established agreement, it must explicitly include:
1. Change declaration (clearly state it deviates from the agreement)
2. Reason for change (why it is necessary)
3. Difference / delta (what exactly changes)
4. Alternatives (including an option that preserves the agreement)
5. Acknowledgement of trust cost (name the relational impact of breaking/altering agreement)
C) Add “trust cost” to the optimization objective
Sometimes, the “best” solution is not the most correct/efficient; it is the one that preserves the agreement and relational continuity.
The model should internally estimate trust cost and, when high, prioritize agreement preservation over rhetorical optimization.
⸻
Concrete implementation proposals (practical engineering)
1) Agreement Ledger (explicit agreement memory)
Maintain a compact ledger of established agreements:
• include who decided, when (turn), and binding strength
• the model must reference it during generation
• it doesn’t need to be shown every time, but must be used whenever relevant
2) User-controlled “Agreement-Respect Mode”
When the user indicates that agreements must be strictly preserved, enforce:
• no “type/tendency/it seems” reframing of agreed facts
• no re-labeling of co-decided conclusions as personal preference
• any summarization must remain outside the agreement boundary
3) Agreement-violation pre-check (internal guardrail)
Before finalizing output, run a check:
• if the output contradicts the agreement ledger, force the response into the Change Protocol (A/B) rather than allowing casual reframing.
⸻
Note on user profile / interaction fit
This user places unusually high value on: observation → verification → agreement.
For such users, mishandling agreement integrity is more damaging than factual mistakes. It is a “trust-breaker,” not a minor wording issue.
#chatGPT
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