200 201 Copyright © Canadian Academy of Oriental and Occidental Culture
Bibliographic record
Abstract
Until recently, the history of women had remained largely neglected in a male dominated society. Thanks to women like Mary Bread and Gerda Lerner who laid the foundation for women’s history to be studied and documented. Works focusing on women gradually swelled bookshelves especially from the nineties of the last century. Some of these scholars, Marion Arnord (1997); Eva Rosander (1997); Nnaemeka and Korieh (2011) have promoted women’s history and placed their roles in correct perspective. This paper, realizing the imbalance in documenting women’s history with museum sources is an attempt at promoting, documenting, and placing in proper perspectives the history of women through the relics found in Jos Museum, Nigeria. The research concludes with an agitation for a Museum of Women’s History to inspire other women to create their own history. It also applauds women for their commitment to the economic, social and political transformation of their societies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.043 | 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 teacher head, 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".