A comparative appraisal of debt relief measures for no income no assets (NINA) debtors in Nigeria
Bibliographic record
Abstract
Nigeria currently has a non-functioning insolvency system; it is yet to record a successful insolvency case. This failure principally is attributable to the weak laws and enforcement policies in existence. The problem is exacerbated by burgeoning consumer debt in the formal sector. The causal factors for this increase in debt are negative economic growth indices such as rising inflation, interest rates and unemployment. With these indices predicted to worsen, a new Bankruptcy and Insolvency Act (BIA) was proposed in 2016. The BIA seeks to regulate individual insolvency proceedings in Nigeria. However, the BIA (as currently conceptualized) does not make provision for debtors with neither income nor assets, often referred to as No Income No Assets (NINA) debtors who, it can be argued, are in the majority in the Nigerian state. The aim in this thesis is to propose debt relief measures that cater for NINA debtors in Nigeria. This proposal aims to prevent further discrimination against these debtors in terms of the current law and the proposed BIA. It envisages that catering for NINA debtors in Nigeria will boost the Nigerian government’s drive to encourage entrepreneurship. In providing for NINA debtors it will provide a safe landing for poor debtors in the event of entrepreneurial failure. The thesis achieves its stated aim by studying international principles and guidelines as espoused by leading bodies. Furthermore, the thesis performs a comparative analysis of relevant NINA provisions in South Africa, Sweden, France, Ireland and Canada. The thesis proposes amendments to the proposed BIA in light of the aforementioned analysis and posits that procedures that are formal and extra-judicial, which have no financial requirements and are easily accessible to debtors should be incorporated.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".