INTERNATIONAL: CompEd 2025 Botswana: Distinctly African with International Standing
Bibliographic record
Abstract
CompEd 2025 Botswana: Distinctly African with International Standing C ompEd is not just another SIGCSE conference; it aims to transform computing education, particularly in the Global South, and in 2025, all roads lead to Gaborone, Botswana.Welcome to CompEd 2025, a distinctively African approach to computing education research with international standing.ACM CompEd is a SIGCSE conference held outside of North America and Europe every other year.It has previously been to Chengdu China and Hyderabad India.CompEd will next be held in October 2025 in Gaborone.CompEd includes many features similar to those of other SIGCSE conferences, including papers, panels, posters, working groups, keynote speakers, a doctoral consortium, and plenty of opportunities for socializing with others interested in computing education.The conference hopes to be relevant to both computing education researchers and practitioners.The mission of the CompEd conference series is to have a lasting positive impact on the computing education community of the local region in which the conference is held.With this in mind, the first thing we did was establish a Regional Programme Advisory Committee with representatives from across Africa.This group developed the background statements for the conference.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.049 | 0.006 |
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".