Proceedings of the International Workshop on Cancer Advocacy for African Countries
Bibliographic record
Abstract
Non-communicable diseases (NCDs) are estimated to be the leading causes of morbidity and mortality in developing countries, especially Africa [ 1 – 3 ]. About 20 percent of the deaths from NCDs in Africans over the age of 45 years of age is from cancer [ 4 ]. Known as Africa’s silent killer, cancer is now a major public health problem in Africa, with the five most frequent cancers being Breast, Cervix, Liver, Prostate and Non-Hodgkin Lymphoma [ 1 ]. However, cancer continues to be underestimated and ignored in Africa. The little attention being given to cancer has led to unnecessary deaths and suffering from cancer, indicating a need for cancer advocacy as one of several strategies for creating awareness of cancer in local and national communities and the need to commit resources aimed at achieving cancer control objectives. Unfortunately, cancer advocacy is currently limited and weak in Africa, thereby making cancer issues of low priority in African countries.
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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 | 0.014 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.035 | 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".