TRACK 2: DEVELOPMENT

National Stakeholders – Nexus Ecosystem

Focus: Engage participants with a strong interest in technology (AI/ML) and development aspects of GCRI’s Nexus Ecosystem.

Week 1: Orientation & Program Launch

• Objective: Familiarize yourself with GCRI’s mission, NWG roles, the Nexus Reports structure, and overarching RTD theme.
• Activities:
  • Learn about NWGs’ function in bridging global and national contexts.
  • Learn about our three tracks (Media, Development, Research) and how they intersect.
  • Slack & Zenodo setup, review of open science and just transition principles.
• Deliverables:
  • Personal development plans: Select your track (Media, Development, or Research).
  • NWG collaboration plan for cross-track synergy.

Learning Components

1. Actor Mapping Guide
   • Usage: Identify essential partners (e.g., government meteorological agencies, data science labs, humanitarian orgs, local communities) critical for AI/ML model inputs or validations.
   • Outcome: Stakeholder map capturing each entity’s role and influence on the two AI/ML projects.
2. System Mapping Overview
   • Usage: Sketch a basic overview of how data flows from local communities to NWG to the global GCRI framework. Identify synergy points for heatwave/displacement response.
   • Outcome: Preliminary system map showing information loops (sensor data, local knowledge, policymaker input) relevant to your AI projects.

Week 2: Tech & Data Onboarding

• Objective: Familiarize with GCRI AI/ML projects, data sources, and desired outcomes.
• Activities:
  • Introduce code repositories (Temporal Fusion Transformer, generative models).
  • Slack channel discussions with project leads on data needs.
• Deliverables:
  • “Project Understanding Document” outlining your NWG’s role in supporting data integration or local calibration.

Learning Components

1. Systems Thinking Guide
   • Usage: Develop an integrated approach for capturing interdependencies (e.g., how heatwaves affect water usage, public health, or displacement triggers).
   • Outcome: A conceptual outline linking your AI/ML workflow to socio-environmental context (energy demand, agriculture yields, community vulnerabilities).
2. Complexity Theory Guide
   • Usage: Recognize emergent and non-linear behaviors in climate events—e.g., how an extreme heat event might unexpectedly shift displacement patterns.
   • Outcome: Early identification of emergent “risk points” your AI model must watch for or incorporate as variables (e.g., farmland dryness, social unrest).

Week 3: Local Data Gathering & QA

• Objective: Identify national data sets relevant to heatwave predictions or displacement scenarios.
• Activities:
  • Collect official meteorological data, displacement logs, health stats.
  • Validate data reliability; note any data gaps or privacy concerns.
• Deliverables:
  • Preliminary “Data Inventory” shared on Zenodo or Slack.

Learning Components

1. Complex Adaptive Systems Guide
   • Usage: Integrate adaptive modules in your code or algorithms so the model re-calibrates as real-time data changes (like sudden temperature spikes or a new flood wave).
   • Outcome: A short technical design doc ensuring model parameters can be updated mid-season or post-event without restarting from scratch.
2. Systems Awareness Guide
   • Usage: Evaluate your own NWG’s readiness—data reliability, stakeholder readiness, local acceptance—keeping in mind how quickly conditions evolve.
   • Outcome: A self-assessment matrix scoring each dimension (data, trust, resource capacity) to guide adjustments in your weekly plan.

Week 4: Model Calibration & Testing

• Objective: Start calibrating project-specific ML models with local data.
• Activities:
  • Use scenario-based small-scale tests (e.g., partial regions).
  • Check parametric settings for mountainous vs. urban areas.
• Deliverables:
  • Beta versions of localized ML scripts or model notebooks.

Learning Components

1. Systems Inquiry Guide
   • Usage: Formulate deeper “why” questions about how local socio-economic or cultural elements shape climate-induced displacement or heatwave vulnerability.
   • Outcome: A refined list of “lines of inquiry” integrated into your data collection or model assumptions (e.g., why certain communities are more heatwave-prone).
2. Systems Theory Guide
   • Usage: Structure your modeling approach using “stocks, flows, feedback loops” language for heatwave/displacement analysis.
   • Outcome: An internal presentation clarifying key variables (stock: population at risk, flow: incoming refugees, feedback loop: community capacity, etc.).

Week 5: Stakeholder Collaboration & Training

• Objective: Train local enumerators, agencies, or community-based organizations on data input protocols.
• Activities:
  • Workshops on model interpretation (e.g., SHAP for explainability).
  • Introduce security or IP guidelines for sensitive data.
• Deliverables:
  • Training materials or user guides for local data contributors.

Learning Components

1. Value Networks Guide
   • Usage: Identify how resource flows—funding, data, technical expertise—circulate among your NWG, philanthropic bodies, local governments, private investors, and communities.
   • Outcome: A “value exchange” diagram clarifying how your AI/ML project is sustained financially and ethically.
2. Multi-Level Mapping
   • Usage: Expand your system map to illustrate how local/municipal data merges into national dashboards (like GCRI’s aggregator) or global risk indices.
   • Outcome: A multi-tier mapping chart that helps project leads or stakeholders see cross-cutting influences (urban heat island, climate migrants, etc.).

Week 6: Integration with GRIx & Just Transition

• Objective: Align your AI/ML work with GCRI’s Global Risks Index (GRIx) for risk-based scenario building.
• Activities:
  • Merge local variables into GRIx risk analytics.
  • Document how your model addresses just transition (e.g., vulnerable farmers, unemployed youth).
• Deliverables:
  • Updated model outputs referencing GRIx layers.
  • “Just Transition Matrix” highlighting benefits for at-risk communities.

Learning Components

1. Horizon Scanning Guide
   • Usage: Spot future changes (e.g., new legislation, shifting migration routes, potential climate tipping points) that could challenge or boost your AI model’s relevance.
   • Outcome: “Horizon Scanning Report” listing plausible short-term (1–2 years) and mid-term (3–5 years) scenarios.
2. Impact Guide
   • Usage: Define how you’ll measure success—are you preventing heat-related mortalities, reducing economic disruption, or mitigating community displacement?
   • Outcome: Clear impact metrics for your AI model (e.g., a 20% drop in undetected heat emergencies, improved resource planning for displaced populations).

Week 7: Model Validation & Peer Review

• Objective: Submit initial results for open review from NWG peers, external experts, or academic labs.
• Activities:
  • Post code and preliminary results on Zenodo with open review requests.
  • Incorporate feedback on accuracy, interpretability, or operational feasibility.
• Deliverables:
  • Revised model with appended review comments, plus updated param files.

Learning Components

1. Leverage Points Guide
   • Usage: Pinpoint critical interventions—like training local staff on how to interpret displacement or heatwave dashboards, or enacting new city ordinances.
   • Outcome: A short strategy doc highlighting 2–3 major “pivot points” for policy or community adoption.
2. Narratives Guide
   • Usage: Craft a storyline that frames your AI project as beneficial, urgent, and equitable—connecting local cultural cues, personal stories, or global frameworks (Sendai, SDGs).
   • Outcome: A media-friendly narrative or success story template for stakeholders, local radio, or NWG discussions.

Week 8: Proof-of-Concept Deployment

• Objective: Implement small pilot or proof-of-concept in a real environment (urban heat island or displacement-prone region).
• Activities:
  • Field testing with local authorities or NGOs.
  • Collect user feedback regarding system usability and reliability.
• Deliverables:
  • Pilot Summary Report: key findings, proposed improvements.

Learning Components

1. Network Organizations Guide
   • Usage: Identify cross-sector alliances that can champion your solutions—like a city-hospital-lab coalition for heatwave alerts or NGO-government synergy for displacement readiness.
   • Outcome: A formal or informal “Network Plan” detailing existing or needed organizational ties.
2. Systems Building Overview
   • Usage: Learn how to architect multi-stakeholder “systems” to keep your AI solutions robust (e.g., data updating, policy feedback, capacity building).
   • Outcome: A blueprint showing how each layer (local teams, NWG committees, national agencies, GCRI) interacts to maintain project momentum.

Weeks 9–10: Further Refinement & Nexus Reports Preparation

• Objective: Finalize your AI/ML solution, including scenario forecasts, for submission to Nexus Reports.
• Activities:
  • Consolidate all code, data sets, user manuals.
  • Cross-check with NWG’s baseline data or policy frameworks to highlight real impacts.
• Deliverables:
  • “Technical Submission Packet” (code, documentation, model validations) for the relevant Nexus chapter.

Learning Components

1. Systems Innovation Guide
   • Usage: Brainstorm creative enhancements—like integrating advanced sensor data for heatwave detection or using generative models to simulate multi-region displacement.
   • Outcome: A short concept paper or pilot test for new innovations (cloud-based dashboards, mobile apps, telehealth synergy in heat crises, etc.).
2. Systems Modeling Guide
   • Usage: Merge your refined data, model prototypes, and advanced techniques (Temporal Fusion Transformer, parametric triggers) into a cohesive modeling environment.
   • Outcome: A near-complete model, validated at least in a pilot region or scenario, with code and initial results posted on Zenodo.
3. Two Loops Guide
   • Usage: Compare the old “loop” of reactive climate response (lack of data or short-term policies) to the new “loop” your model fosters (proactive, data-informed, just).
   • Outcome: A visual or infographic for your NWG illustrating how your project replaces outdated processes with a resilient approach.
4. Systems Change Overview
   • Usage: Build a broader strategy for long-term system transformation—embedding your AI solutions in national or city-level climate frameworks.
   • Outcome: Draft “Systems Change Plan” explaining how your AI solutions can eventually integrate with local governance, investor frameworks, or philanthropic expansions.

Weeks 11–12: Final Evaluation & Future Roadmaps

• Objective: Evaluate end-to-end performance, plan expansions or advanced features.
• Activities:
  • Compare results with real-world conditions (for heatwaves or displacement events).
  • Draft a roadmap for scaled-up coverage or advanced AI integration.
• Deliverables:
  • “AI/ML Project Final Documentation” + next-step proposals for policy integration or advanced model deployment.

Learning Components

1. Guides Overview
   • Usage: Conduct a quick “meta” reflection. Which guides were most valuable? Are any steps or theories underapplied? Are your final solutions consistent with RTD, Earth system boundaries, or just transition?
   • Outcome: A summarized matrix aligning each resource (Systems Thinking, Horizon Scanning, etc.) with how you used it, any follow-up tasks needed.
2. Scaling Change Guide
   • Usage: Outline the roadmap for mainstreaming your heatwave or displacement model—expanding to new geographies, forging national alliances, or adopting policy levers.
   • Outcome: A final “Scaling & Sustainability Plan” plus any concluding documentation posted on Zenodo for the quarterly submission.

Putting It All Together:

During these 12 weeks, you’ll develop or refine AI/ML solutions addressing:

1. Prediction Models:
   • Ensuring robust spatial-temporal modeling, real-time data integration, stakeholder acceptance, and communication strategies.
2. Impact Risks:
   • Combining multi-level data, scenario-based forecasts, and stakeholder alliances to mitigate service disruptions in receiving communities.

Each resource (Actor Mapping, Complexity Theory, Systems Innovation, etc.) shapes your approach, ensuring you create solutions that are:

• Technically Sound: Calibrated with local data, tested for reliability, and scaled effectively.
• Socially Inclusive: Rooted in just transition, accounting for front-line communities.
• Environmentally Responsible: Aligned with Earth system norms, respecting planetary boundaries.
• Politically and Culturally Attuned: Engaging relevant ministries, NGOs, indigenous leadership, philanthropic or private sector partners.

By weaving these resources weekly into your AI/ML tasks, you transform a purely technical project into a holistic development initiative bridging climate justice, sustainable resource management, and the Right to Development.

End goal: Have your code or final deliverables polished, your local partnerships well-established, and your results ready for Nexus Reports quarterly submissions.

Choosing Your Track & Next Steps

1. Select the track that best aligns with your skills and goals.
2. Follow the weekly guidelines but remain flexible—collaboration across tracks is encouraged!
3. Engage with NWG leadership, Slack channels, and Zenodo submissions for feedback and synergy.
4. Aim to have final outputs ready for submission, supporting the global theme: Climate Justice, Sustainability, and the Right to Development.

By the end of 12 weeks, you’ll have tangible contributions—media outreach materials, AI solutions, or a published research piece—firmly anchored in open science, just transition, and Earth system stewardship.

We look forward to your active participation and pioneering achievements within your chosen track!