Mothering Practices and Human Rights Abuses of Girls in Marginalized Contexts: Examining the Moral Culpability of Women who Abuse Children
Bibliographic record
Abstract
The form of gender-based violence (GBV) analysed in this article relates to ‘harmful traditional practices’ (HTPs) detrimental to the health and well-being of girls. In response to calls to action from the international community, many states are enacting laws and regulations against HTPs. Scholars argue that criminal justice reform is a vital component of efforts to reduce HTPs. While this article supports establishing mechanisms to address violence against girls, I question whether criminalization is a suitable strategy. HTPs, although classified as GBV, have distinctive features. Their prevalence hinges on ideas surrounding ethnicity and/or religion in addition to the fact that women work directly and indirectly to sustain these violent practices. The developing contemporary understanding of HTPs within and without legal systems, to the degree that it focuses on perpetrators of abuse, ignores the role of women in the production and reproduction of HTPs. Criminalization of HTPs will have a disproportionate effect on mothers. This article seeks to question and understand the dilemma of determining the ‘moral culpability’ or responsibility of mothers who support and participate in violence against girls. Using postmodern feminism, I argue that a genuine commitment to protecting children requires an approach that accounts for the growing significance of culture, not only in defining action but also providing cultural components that play a role in the decisions of mothers.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".