Inventing Legal Combat: Pro-Poor “Struggles” in the Human Rights Jurisprudence of the Nigerian Appellate Courts, 1999–2011
Bibliographic record
Abstract
This article deals with the question whether the jurisprudence of Nigeria’s appellate courts has helped advance or impede the struggles of the poor to assert their human rights in the country. The article begins by defining, delimiting, and situating the concepts “struggle” and “human rights as struggle.” It then moves on to identify and discuss the factors that make the struggles that the poor and the subaltern must wage to realize their human rights a tough one. Following this discussion, the article turns its attention to its main focus, i.e., an analytical examination of the ways in which the corpus of human rights jurisprudence of the Nigerian appellate courts has either aided and/or inhibited the struggles of the poor and the subaltern in that country during the period under study. The latter discussion is sub-divided into two segments: the first is focused on the engagement of these courts with the pro-poor struggles of Nigerian Labour, while the second is devoted to an analysis of the attitude of the courts to other kinds of pro-poor human rights struggles in Nigeria. In both cases, given space and other constraints, only small but representative samples of the relevant cases are discussed.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.022 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".