Gender Specifics and Social Relations in North-Eastern Yorubaland: Isua Akoko Example, up to 19th Century
Bibliographic record
Abstract
A unique feature of Akokoland is the divergent origins of the people. Like other Akoko communities with multifarious historical backgrounds, the history of origin of Isua Akoko is also shrouded in migrations from different areas: Yoruba and Edo. A major corollary of these two fundamentally different regions is: cultural fluidity in Isua Akoko social relations which cut across several aspects of the people’s life such as marriage, widowhood, divorce and especially age-grade. It is on this backdrop that this paper, from the gender perspective, analyses the social grouping of males and females in Isua Akoko with the aims of showcasing the cultural fluidity, especially, in the female grouping and its implications. For instance, while social grouping of males was based primarily on “age” as elsewhere in Yorubaland, grouping of females was, however, largely premised on marriage and motherhood. The work is approached from historical perspective with the methodology of narration and critical analysis of data. Theory of historical feminism is used to interpret gender narratives in the sources. The work submits that the gender specifics of Isua Akoko, a Yoruba community is distinct from Yoruba cultural identity in social relations. Therefore, Isua Akoko does not conform to cultural uniformity of Yoruba in gendering of its social relations.
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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 | 0.000 | 0.000 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".