Women’s Survival in Ghana: What Has Law Got to Do With It?
Bibliographic record
Abstract
In the 21st century, women around the world continue to face societal, economic, and sexual adversities, even as the “#MeToo” and “Time’s Up” movements appear to have excited a new tone toward the fight for gender equality and female empowerment. In this context, and in the broader context of liberating women from the oppressive shackles of a world that seems to punish women for their mere existence, this article tackles issues which are mainly peculiar to Ghana, and which have an impact on the equality, empowerment, and survival of women. The article discusses the laws, policies, and regulations that have been established to improve the lives of women in Ghana. It also examines the scourge of dangerous, discriminatory practices such as female genital mutilation, the banishment of suspected witches, and inhumane widowhood rites, which are detrimental to the survival of women in Ghana. The article recognizes that the lack of sustained growth in women’s progress globally, and specifically in Ghana, is due in large part to these discriminatory practices that have muted the voices and perspectives of women in decision-making. The law, therefore, is not enough. Some of the pivotal roles nongovernmental organizations (NGOs) have played to empower women include campaigns to repeal legislation adversely affecting women’s inheritance and to improve the fairness of the Parliamentary process, efforts toward the elimination of trokosi, and the initiatives to protect women suspected of witchcraft from humiliation and banishment.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| 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".