Bibliographic record
Abstract
Living and practising medicine in East Africa has been an eye-opening experience. Before coming abroad, I had spent a lot of time in northern Canada doing residency electives and practising family medicine. I always imagined that rural African clinics would be very similar to the isolated nursing stations sprinkled throughout Canada’s northern communities. For instance, they would be like the tiny station in Repulse Bay, on the Arctic circle, where I worked for a summer. As it turns out, I could not have been more mistaken. I fi rst came to Uganda 5 years ago and worked with a non-governmental organization in the remote African community of Rakai in southwestern Uganda. Rakai has a measure of notoriety as the fi rst place in Africa— and in the world—to fully experience the AIDS epidemic. Some believe it is where the very fi rst cases were diagnosed. Hundreds of villages were wiped out by the devastating disease that the locals call “slim,” after the eff ect it has on the bodies of the affl icted. Th e hospital for the area is called Kakuuto Health Centre. It has separate wards for male and female patients, a pediatric ward, a one-room operating theatre, a laboratory, and an outpatient clinic. Despite these impressive designations, the hospital has no running water and no electricity. A solarpower array, donated by Medicine du Monde several years ago, now lies broken, its regular maintenance costs far beyond the health centre’s meagre budget. Th e ceilings of the wards are black from the smoke of candles and lanterns used by patients at night. Family members are responsible for feeding patients. Th e few beds that are in the wards have no mattresses, and the fl oors are covered in bedrolls and plastic eating containers of all colours and descriptions.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".