Bibliographic record
Abstract
In 2008, I had the opportunity to go to Ethiopia (Fig. 1) to assess current medical library resources and services provided at the Black Lion Hospital, which is the major teaching hospital for Addis Ababa University (AAU), Addis Ababa, Ethiopia.I attended the Addis Ababa International Educational Symposium, Addis Ababa, Ethiopia, where the Ethiopian Ministry of Education put forth the desired and ambitious goal of increasing Ph.D. graduates in all disciplines by 5000 and Masters graduates by 10 000 within the next 10 years (medical subspecialists are included with Ph.D.s under this requirement).Universities worldwide with an interest in educational partnerships were invited to this symposium in order to help AAU meet its goal of dramatically increasing its number of academics.My invitation came from Dr. Clare Pain from the
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.115 | 0.023 |
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".