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Record W2914529320 · doi:10.1177/0306312719829595

Situating science in Africa: The dynamics of computing research in Nairobi and Kampala

2019· article· en· W2914529320 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSocial Studies of Science · 2019
Typearticle
Languageen
FieldComputer Science
TopicICT in Developing Communities
Canadian institutionsConcordia University
FundersDivision of Social and Economic SciencesEconomic and Social Research Council
KeywordsTransformative learningSociologyEthnographyPoliticsSocial sciencePublic relationsPolitical sciencePedagogy

Abstract

fetched live from OpenAlex

Since the turn of the century, both Kampala and Nairobi have experienced a dramatic growth of computer science research, challenging accepted views of science in Africa. We deploy qualitative methods to follow active computer science researchers, graduate students, policy makers, administrators and entrepreneurs, in order to understand how computer science is enacted in these two cities. Our analysis focuses on four interrelated areas of labor, institutions, identities and scale. We illustrate the dynamics and frictions of computer science research across these areas, revealing the interlacing of moral economies of science and the political economy of higher education, the management of precarious professional lives and desire to get research done, and the pluralistic imaginations and multiple scales of computer science. Urban centers in East Africa are increasingly active in supporting granular and connective research communities that are socially transformative in ways that challenge conventional views of Africa as technologically dry. In this way, the computer science communities of Nairobi and Kampala are instructive for thinking about new geographies of science and technology studies.

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.

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaScience and technology studies
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativelow
gptScience and technology studies
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativehigh
models agreeAgreement compares identical category sets and study designs across arms.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.507
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.006
Science and technology studies0.0010.013
Scholarly communication0.0000.001
Open science0.0030.004
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.151
GPT teacher head0.400
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it