Engaging with uncertainty: Information practices in the context of disease surveillance in Burkina Faso
Bibliographic record
Abstract
Uncertainty is inherent to outbreaks of infectious diseases; a topic of global concern. Addressing global outbreaks requires – among other things – well-functioning systems to produce information. The aim of the paper is to understand uncertainty in the context of information systems (IS) and to analyze the role of formal and informal information practices in identifying and responding to communicable diseases in the context of developing countries. Our empirical focus is on a dengue outbreak in 2016 in Burkina Faso- Dengue was then unknown in the context and formal “techne” based information systems were inadequate in dealing with it. Drawing on work defining uncertainty as a resource, we extend our practice-based theoretical lens with the concepts of “general and specific metis” to describe practices neither established formally or informally, but which evolve as the disease unfolds. While general metis represents practices based on the broader understanding of the context which the health staff have, specific metis relates to the particular practices they construct to acquire, share, and react on information as the disease unfolds. Our paper contributes primarily in foregrounding the role of uncertainty in information systems research and how this relates to formal, informal and emerging information practices.
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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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".