An Integrated Information System of Climate-Water-Migrations-Conflicts Nexus in the Congo Basin
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We present an integrated information system needed to address the climate-water-migration-conflict nexus in the Congo Basin. It is based on a rigorous and multidisciplinary methodological approach that consists of designing appropriate tools for field surveys and data collection campaigns, data analysis, creating a statistical database and creating a web interface with the aim to make this information system publicly available for users and stakeholders. The information system developed is a structured and organized set of quantitative and qualitative data on the climate-water-migration-conflict nexus and gender, consisting of primary data collected during field surveys. It contains 250 aggregated variables or 575 disaggregated variables, all grouped into 15 thematic areas, including identification; socio-demographic characteristics; access to resources; perception of climate change; perception of migration; financial inclusion (savings, access to credit and circulation of money); domination and control on water resources, land ownership and property rights, conflict resolution and community resilience; water uses; vulnerability to climate change; housing, household assets and household expenditure; food security; health, hygiene and sanitation; environmental risk management; women’s economic autonomy; and water transfer from the Congo Basin to Lake Chad. The information system can be used to model and understand the interface of human-environment interactions, and develop scenarios necessary to address the challenges of climate change and resilient development, while supporting key policy areas and strategies to foster effective stakeholder participation to ensure management and governance of climate and natural resources in the Congo Basin.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it