Bibliographic record
Abstract
Africa has always been an important source of rich information for knowledge production. There has always been a curiosity about Africa that has served different imaginations and interests. But how do we learn and teach about Africa in ways that are informed by an appreciation of African peoples’ rich cultural knowledges, com- plexity and historic resistance of local peoples to carve out their own futures and dreams? I would maintain that knowing about education and socialization offer some important directions in this search for knowledge. Tradi- tional African education has utilized a variety of instructional and pedagogic methods as well as guides and resources to educate youth. Education in African communities has happened in multiple sites, formal and non-formal. Just as West African education can benefit from a study of educational delivery in other contexts, I would argue that a study of important aspects of West African formal and non formal education and socializa- tion of young learners may offer significant lessons for educating youth in other societies. There is intellectual relevance in asking such questions as: What and how do students in West African learn? What activities, stories do students experience in their education that can be incorporated into the curriculum to enrich educating stu- dents from diverse backgrounds in other contexts? What is the nature of the environment in which students learn in West Africa?
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".