(Non)negotiable spaces of algorithmic governance: Perceptions on the Ubenwa health app as a ‘relocated’ solution
Bibliographic record
Abstract
This study explores relocated algorithmic governance through a qualitative study of the Ubenwa health app. The Ubenwa, which was developed in Canada based on a dataset of babies from Mexico, is currently being implemented in Nigeria to detect birth asphyxia. The app serves as an ideal case for examining the socio-cultural negotiations involved in re-contextualising algorithmic technology. We conducted in-depth interviews with parents, medical practitioners and data experts in Nigeria; the interviews reveal individuals’ perceptions about algorithmic governance and self-determination. In particular, our study presents people’s insights about (1) relocated algorithms as socially dynamic ‘contextual settings’, (2) the (non)negotiable spaces that these algorithmic solutions potentially create and (3) the general implications of re-contextualising algorithmic governance. This article illustrates that relocated algorithmic solutions are perceived as ‘cosmopolitan data localisms’ that extend the spatial scales and multiply localities rather than as ‘data glocalisation’ or the indigenisation of globally distributed technology.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".