Bibliographic record
Abstract
Dubbed Silicon Savannah, Nairobi has become a hot spot of tech development that promises to “save Africa.” Qualitative research—carried out by a tangle of private, academic, and non-profit organizations—is part of the work, promising to reveal how people in Kenya are building and benefiting from a dazzling array of digital products. Amidst the enthusiasm, longstanding problems with ways in which research data in Nairobi is conceived, collected, and shared are easily glossed over. This article advances thinking about the politics of qualitative data, unraveling normative concepts like ethics and transparency by both examining existing data practices and modeling alternatives. I describe the sociotechnical infrastructure underlying the ethnographic project, detailing tactics for deploying an instance of open source software—the Platform for Experimental, Collaborative Ethnography (PECE)—to draw research interlocutors into collaborative effort to understand and build decolonized qualitative data infrastructures. Through such processes I learned that collaborating on data not only refreshes the social contract of qualitative work; it can also enhance its robustness and validity. I advise scholars to better document our own knowing practices in order to attend to the inevitability of margins created through all data practices, including our own
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 | 0.117 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.025 | 0.128 |
| Scholarly communication | 0.019 | 0.025 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".