Envisioning an African-centric higher education home economics curriculum for the 21st century
Bibliographic record
Abstract
Home Economics as a discipline of study was introduced into Africa mainly through missionary activity in the first half of the 20th century. However, training educators to teach in higher education institutions on African soil did not begin until the last half of the 20th century. To fill this gap, teachers were trained abroad, mostly in England, Canada and the United States. They then returned to teach in African institutions. As a result, the content of most Home Economics higher education and public school programmes in Africa reflects a strong Western influence. The relevancy of these programmes has oftentimes been criticized, warranting programme evaluation and review. Africa is a unique continent (54 countries) with particular challenges for its individuals and families. While embracing similarities and the leadership provided by Western paradigms, African home economists need to be sensitive to these unique challenges and to their own culture in order to create an appropriate curriculum that is relevant to their context. This chapter first explores the current challenges that are faced by Black African families in the Southern African Development Community (SADC) region. This overview is followed by a discussion of the challenges that emanate from professional Home Economics practice. It concludes by envisioning an ideal Africentric Home Economics higher education curriculum for socializing future generations of African home economists.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".