Modest harvests: on the significant (but limited) impact of human rights NGOs on legislative and executive behaviour in Nigeria
Bibliographic record
Abstract
This article argues that the community of self-described human rights NGOs that are indigenous to and operate in Nigeria have, over the last fourteen years or so, exerted a modest yet significant level of influence on the behaviour of both the executive and legislative branches of government in Nigeria. This influence has been exerted despite the harshness of military regimes that ruled that country for most of the relevant period. The factors that have facilitated the modest impact that these NGOs have had within Nigeria include their ability (at times) to work in coalitions; the highly innovative ends-means calculations made by NGO activists; their virtual alliance with a wing of the Nigerian judiciary and an important section of the mass media; their ability to creatively deploy international texts, processes and pressure within Nigeria; the deep cleavages within the Nigerian state and its elite that have led to a political culture that is much more amenable to negotiated settlements than is commonly acknowledged in the literature; and the unrelenting disposition, remarkable tenacity, and courage of most of the activists that run and operate these NGOs. The factors that have limited the extent of the success that these NGOs have so far enjoyed are conceptual and institutional in nature. The conceptual factors are their (albeit changing) tendency in this context to treat socio-economic, gender-related, minority, and environmental rights work as more marginal than civil/political rights activism; and their failure to pay any appreciable attention to the aspects of the local cultures and pre-existing languages of dignity that might advance their work among Nigeria's majority rural population. The institutional factors include their excessive fragmentation and proliferation; the de-institutionalization of most of their organizational forms and resort to personal rule; their donor-dependence and consequent failure to canvass the local population for funds and resources; and the elite and urban-centred orientation of most of these NGOs. Each of these factors has affected negatively their ability to mass mobilize and connect deeply with the yearnings of the masses of ordinary Nigerians.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".