Visualizing relationships in interdisciplinary research with Geographic Information Systems: A case study utilizing food security research in Sahelian West Africa
Bibliographic record
Abstract
Achieving food security in the semi-arid region of West Africa remains challenging, primarily due to a combination of harsh climate and low soil fertility. The University of Saskatchewan, in partnership with many international organizations, has been researching solutions to increase the profitability of smallholder farmers in the region. This joint partnership aims at improving soil fertility for smallholder farming in Benin, Burkina Faso, Mali, and Niger. Data have been collected in eight different research sites, and include rainwater harvesting, crop yields, and soil samples. The data have a timeframe ranging from one year to many years. Prior to the University of Saskatchewan’s commitment to the project, research data had only been utilized on a local scale, with relatively low success in sharing results and findings across national borders.With this project, collaboration occurred among multiple researchers from different countries and disciplines. A new technique used for collaboration was an interactive Geographic Information Systems (GIS) database. GIS has proven to be a powerful tool and platform for analysing and disseminating research data. This geospatial analysis laid the foundation for further research, resulting in a robust examination of soil and socioeconomic data from overseas.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".