The DSAIL-GeJuSTA Data Science Education Workshop: Designing a Data Science Curriculum for the African Continent
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.
Bibliographic record
Abstract
The DSAIL-GeJuSTA Data Science Education Workshop was a joint initiative by the Centre for Data Science and Artificial Intelligence (DSAIL) and Gender Justice in STEM Research in Africa (GeJUSTA). GeJUSTA is a programme funded by the International Development Research Centre (IDRC) that is working towards increasing the representation of women in STEM. The workshop was held on 9 November 2023, during the 7 th DeKUT International Conference on Science, Technology, Innovation and Entrepreneurship (STI&E) at Dedan Kimathi University of Technology (DeKUT). The conference ran from 8-10 November 2023. The event successfully convened 31 participants. The composition of the attendees was diverse, ranging from data-science educators, industry participants using data science, researchers who use data science and students in a myriad of courses, including engineering and pharmacy. The primary focus of the workshop was to have a discussion with the attendees and share practices around designing data-science curriculum, strategies for achieving gender equity in data-science education, addressing new technological challenges in education and fostering multidisciplinary approaches to data-science education. This report encapsulates the collective vision of the workshop participants, whose contributions have set the stage for progressive strides in data-science education.
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.
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 | 0.014 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it