Bibliographic record
Abstract
An opportunity to travel to Africa with a mission team to work in a health clinic presents itself.You ask yourself, why not?Is it because you don't have enough money for the airfare or immunizations?Is it that you can't see yourself leaving family or children?When you ask yourself why travel to Africa, the answers can start you on a journey of self-discovery.Overcoming the "why nots" can be easier than you think.Funding can be obtained from different sources if you start early.You'd be surprised at how many of your colleagues will support you in picking up or switching your shifts.As for your children, your experiences will rub off on them and expand their world as well.Decision made!After 41 hours, three planes, and a two-hour van ride, we found ourselves at the Lifeline Malawi Health Clinic in the fishing village of Ngodzi on Lake Malawi.Our Calgary mission team was made up of seven nurses, one construction expert, one cameraman and one pastor.Basically, we were to provide medical care, construction assistance and spiritual support for the Malawian people in the area.The country of Malawi is wedged between Zambia, Tanzania and Mozambique.Most of its eastern border is formed by Lake Malawi, the third largest lake in Africa.The population of Malawi is more than 12 million with an average life expectancy of 30 years.There are only about 150 doctors in the entire country.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.039 | 0.004 |
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".