An analysis of the African cancer research ecosystem: tackling disparities
Bibliographic record
Abstract
Disparities in cancer research persist around the world. This is especially true in global health research, where high-income countries (HICs) continue to set global health priorities further creating several imbalances in how research is conducted in low and middle-income countries (LMICs). Cancer research disparities in Africa can be attributed to a vicious cycle of challenges in the research ecosystem ranging from who funds research, where research is conducted, who conducts it, what type of research is conducted and where and how it is disseminated. For example, the funding chasm between HICs and LMICs contributes to inequities and parachutism in cancer research. Breaking the current cancer research model necessitates a thorough examination of why current practices and norms exist and the identification of actionable ways to improve them. The cancer research agenda in Africa should be appropriate for the African nations and continent. Empowering African researchers and ensuring local autonomy are two critical steps in moving cancer research towards this new paradigm.
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How this classification was reachedexpand
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".