(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.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".