Core Competences and Optimising Bank Capital Management in Nigeria
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study explores empirically how Core Competences predicts efficient bank capital management in the Nigerian banking industry. We adopted Competence predictors namely, knowledge, skill and attitude as our independent variables and Return on Capital Employed (ROCE), the common method of measuring the size of returns derived from capital funds as our dependent variable. The empirical evidence obtained revealed a dwindling state of employees’ core competences, meaning that the necessary knowledge, skill and attitude required to efficiently manage bank capital were lacking in a good proportion of employees under investigation. Of course, the empirical results may be the reflection of the recruitment strategy that undermined professionalism in academic qualification of bank employees which has been prevalent in the industry from the 1980s and may also serve as a lesson for relevant authorities in the industry.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it