Interpreting Women’s History with Museum Sources: An Experience in a Nigerian Museum
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. Key words: The history of women; Imbalance; Museum sources; Political transformation
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.042 | 0.017 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".