Troubled Assets Resolution: In Search of the Best Approach?
Bibliographic record
Abstract
The critical day-to-day situations the Asset Management Corporation of Nigeria (AMCON) is faced with in ensuring that it fulfills its statutory mandate of efficiently managing and disposing of acquired eligible bank assets (EBAs) are deserving of great managerial as well as consensual and quasi-consensual asset resolution skills. It is against this background that this paper examines the concept of asset resolution with particular regard to consensual and quasi-consensual asset resolution mechanisms. Using AMCON as a case study and drawing relevant examples from Italy, China and Indonesia, this paper makes a case for the best approach to be adopted in asset resolution. The paper concludes that the choice of an “optimal” asset resolution method will continue to surface as topic of considerable debate, but that the “best” approach would be to adopt a mix of resolution options and treat each case as it arises.
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 | 0.026 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.022 | 0.022 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".