Regulating Gender Violence in Postcolonial Societies: Is Legal Pluralism a Problem for Human Rights?
Bibliographic record
Abstract
Abstract This article is an examination of the pluralistic context in which human rights laws operate. The existence of unequal but mutually constitutive legal and normative orders in diverse societies raises complex issues for human rights practitioners. As the language of rights acquires worldwide currency, questions increasingly arise as to their relationship with other normative orders and systems of justice. While the impetus for legal pluralism may be multifaceted and complicated, this article focuses exclusively on the human rights dimension in Africa. Its main objective is to explore the nature of the relationship between gender equality and legal pluralism. In plural settings, the conflict between the need to preserve minority culture and the protection of rights forms the basis for how legal reforms have failed to address issues affecting women and girls. To ensure a consistent application of human rights norms, States must acknowledge that there are severe limits and resistance to formal laws within society, that there are spaces where its authority is not absolute and that ‘legal pluralism is a fact’. Failing to do this results in the development of new forms of ‘identity politics’ that continue to silence and subordinate women and girls.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.064 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".