Bibliographic record
Abstract
Joseph Nsengimana is Director of the Mastercard Foundation Center for Innovative Teaching and Learning. The Mastercard Foundation is a private Canadian foundation—one of the largest in the world—with a mission to advance access to education and deepen financial inclusion. The foundation’s work has been focused on Africa since 2009, reaching hundreds of millions of people. In 2018, the foundation launched a strategy called Young Africa Works, laying out a bold plan to enable 30 million young people in Africa, particularly young women and groups facing the highest barriers to opportunity such as displaced or disabled youth, to access dignified and fulfilling work by 2030. The Mastercard Foundation Center for Innovative Teaching and Learning helps to drive this goal by advancing the use of technology to deliver at-scale access to relevant education and skills training, a precondition for dignified work. The education sector is likely to be significantly impacted by the mainstreaming of AI. This drives Mr. Nsengimana’s interest in human-centered AI.
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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.035 | 0.065 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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".