Text Analysis and Knowledge Structuring
Support research and analytical work on disaster risk reduction and climate resilience.
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Overview
Support research and analytical work on disaster risk reduction and climate resilience.
You have:
- At least Secondary education or equivalent. A bachelor's degree in environmental science, information science, data science, or related fields is an important asset.
- Experience conducting desk research, document analysis, policy analysis, literature reviews, or related analytical work.
- Ability to review and synthesize large volumes of information, identify patterns and relationships across documents, and organize qualitative information into structured categories and taxonomies.
- Familiarity with climate adaptation, disaster risk reduction, resilience, National Adaptation Plans (NAPs), Nationally Determined Contributions (NDCs), or sustainable development policy processes is desirable.
- Understanding of concepts related to text analysis, semantic similarity, multilingual document analysis, classification, embeddings, chunking, context windows, large language models, or related approaches is considered an asset.
- Fluency in English is required. Knowledge of Russian, or any language from the RBEC region is an advantage.
Contract
This is a UNV contract. More about UNV contracts.
The Online Volunteer will be part of UNDP’s Climate and Disaster Resilience Team at the Istanbul Regional Hub, supporting research and analytical work on disaster risk reduction, climate resilience, and risk-informed development across Europe and Central Asia. The findings will contribute to ongoing analytical work on country risk profiles.
The Online Volunteer will support the analysis and structuring of climate and disaster resilience commitments contained in national policy and planning documents. The assignment will focus on improving the consistency, comparability, and interpretation of commitments and targets identified across countries, sectors, and languages.
Methodology Review: Review existing approaches used by international organizations, researchers, and policy initiatives to classify and analyze policy commitments, targets, adaptation measures, and resilience actions.
Knowledge Structuring: Support the development of thematic and sectoral classification approaches for organizing climate adaptation, disaster resilience, and risk reduction commitments into comparable categories.
Semantic Analysis: Review examples of commitments and targets and assess approaches for grouping similar actions despite differences in terminology, wording, level of detail, or policy context.
Comparative Assessment: Identify common patterns, overlaps, and variations in commitments across countries and regions, and assess their implications for cross-country comparison.
Localization and Interpretation: Assess challenges related to multilingual policy documents, country-specific terminology, translation differences, and policy context that may affect the interpretation, classification, and comparability of commitments across countries.
Validation and Quality Review: Conduct sanity checks on outputs, review classification results, and provide feedback on consistency, interpretability, and practical applicability.
Research Outputs: Prepare concise summaries, methodological notes, and recommendations highlighting findings, challenges, and opportunities for improving consistency and comparability.
Education/Field of Academic Study: At least Secondary education or equivalent. A bachelor's degree in environmental science, information science, data science, or related fields is an important asset.
Research Skills: Experience conducting desk research, document analysis, policy analysis, literature reviews, or related analytical work.
Analytical Skills: Ability to review and synthesize large volumes of information, identify patterns and relationships across documents, and organize qualitative information into structured categories and taxonomies. Experience working with structured or unstructured textual information, classification frameworks, taxonomies, semantic analysis, content analysis, or comparative analysis is desirable.
Knowledge Areas: Familiarity with climate adaptation, disaster risk reduction, resilience, National Adaptation Plans (NAPs), Nationally Determined Contributions (NDCs), or sustainable development policy processes is desirable.
Digital and AI Literacy: Understanding of concepts related to text analysis, semantic similarity, multilingual document analysis, classification, embeddings, chunking, context windows, large language models, or related approaches is considered an asset.
Language skills: Fluency in English is required. Knowledge of Russian, or any language from the RBEC region is an advantage.
Potential interview questions
| Describe a situation where you had to conduct desk research and what strategies you used. | This question aims to evaluate your research skills and methodology. | Discuss specific methods you employed, tools you used, and how you organized your findings. |
| Provide an example of how you analyzed and synthesized large volumes of information. | The interviewer wants to understand your analytical capabilities. | Pro members can see the explanation. |
| Can you explain a time you had to deal with multilingual documents? How did you manage the challenges? | Pro members can see the explanation. | Pro members can see the explanation. |
| Give an example of how you have contributed to policy analysis in the past. | Pro members can see the explanation. | Pro members can see the explanation. |
| How do you ensure the accuracy and quality of your research outputs? | Pro members can see the explanation. | Pro members can see the explanation. |
| Describe a project where you had to classify or categorize information. What was your approach? | Pro members can see the explanation. | Pro members can see the explanation. |
| Discuss your experience with structured or unstructured textual information. | Pro members can see the explanation. | Pro members can see the explanation. |
| What challenges have you faced in your previous research, and how did you overcome them? | Pro members can see the explanation. | Pro members can see the explanation. |
Added 1 month ago
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Updated 12 days ago
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Source:
unv.org
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