Representations of an Ebola ‘outbreak’ through Story Technologies
Bibliographic record
Abstract
Background Attempts to understand biosocial phenomena using scientific methods are often presented as value-neutral and objective; however, when used to reduce the complexity of open systems such as epidemics, these forms of inquiry necessarily entail normative considerations and are therefore fashioned by political worldviews (ideologies). From the standpoint of poststructural theory, the character of these representations is at most limited and partial. In addition, these modes of representation (as stories) do work (as technologies) in the service of, or in resistance to, power. Methods We focus on a single Ebola case cluster from the 2013–2016 outbreak in West Africa and examine how different disciplinary forms of knowledge production (including outbreak forecasting, active epidemiological surveillance, post-outbreak serosurveys, political economic analyses, and ethnography) function as Story Technologies. We then explore how these technologies are used to curate ‘data,’ analysing the erasures, values, and imperatives evoked by each. Results We call attention to the instrumental—in addition to the descriptive—role Story Technologies play in ordering contingencies and establishing relationships in the wake of health crises. Discussion By connecting each type of knowledge production with the systems of power it reinforces or disrupts, we illustrate how Story Technologies do ideological work. These findings encourage research from pluriversal perspectives and advocacy for measures that promote more inclusive modes of knowledge production.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".