Teaching “Human Rights in Africa” Transnationally: Reflections on the Jos-Osgoode Virtual Classroom Experience
Bibliographic record
Abstract
During the Fall of 2007, as part of a much broader York-Nigerian Universities linkage project that he had been working on for some time, Professor Okafor taught an internationalized version of a pre-existing existing course entitled “Human Rights in Africa.” At the same time, Professor Dakas of the Faculty of Law, University of Jos, Nigeria (assisted by Mr. J.D. Gamaliel) taught a similarly modified version of an existing course at their own institution. Professor Dakas, a former Hauser Global Scholar at New York University and most recently the attorney-general of the Plateau State of Nigeria) was the lead faculty at that partner law school in Nigeria.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.030 | 0.015 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".