SayPro Annotated Research Notes

SayPro Annotated Research Notes – Standard Format

Purpose: To capture insights from reports, interviews, or GPT-generated outputs, with annotations explaining context, reliability, relevance, and suggested next steps.

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🧾 TEMPLATE STRUCTURE

Section	Description
Title	Clear, specific topic title (e.g., Urban Adaptation to Flooding in Lagos)
Date	YYYY-MM-DD
Source Type	Literature review, fieldwork, GPT prompt, government report, etc.
Author/Researcher	SayPro fellow/employee name
Related Program	e.g., Youth Climate Action, Climate Finance Watch, Adaptation Toolkit

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✍️ EXAMPLE ENTRY

Title: Community-Based Flood Adaptation in Informal Settlements

Date: 2025-06-04
Source: GPT Output + UN Habitat 2024 Report
Author: Thabiso Mokoena
Program: SayPro Climate Adaptation Fellows

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📄 Key Findings

Observation	Annotation	Source
Community-led drainage systems reduce local flood impacts by 60%	Particularly effective in high-density, low-income areas. Requires initial NGO facilitation.	UN Habitat Report 2024
Lack of land tenure limits formal adaptation investment	Policy reform needed; risk of eviction discourages infrastructure upgrades.	GPT Summary + Urban Studies Review
Women often lead informal early warning systems	Opportunity to formalize and fund these efforts. Gender integration needed in policy.	Interviews (SayPro Uganda Team)

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📌 Researcher Notes

• Relevance: High for SayPro’s slum resilience strategy in 2025–2026.
• Reliability: Verified by cross-checking GPT output with UN sources and field notes.
• Next Steps:
  • Share case example in next SayPro Urban Adaptation Toolkit.
  • Explore micro-grant options for CBO-led infrastructure upgrades.

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📎 Attachments

• Annotated bibliography
• Related visuals or data graphs
• PDFs or transcripts (if applicable)

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✅ Review & Approval

Reviewed by	Date	Comments
[Team Lead Name]	[YYYY-MM-DD]	Approve for inclusion in quarterly sector brief

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🔄 Usage Scenarios

• Input for monthly research summaries
• Content for training materials
• Source material for fellowship case studies
• Verification against GPT-generated outputs

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