Partial Ordered Logit Analysis of Confidence Levels in Financial Institutions in Ghana. The Case of Asante Mampong Municipality
Bibliographic record
Abstract
In recent times the financial sector (FS) of Ghana has been saddled with liquidity and operational challenges leading to several financial policies put in place by the Central Bank. The financial crisis and its resultant stringent measures affected public confidence as many customers lost their investments/savings while some financial institutions were consolidated or collapsed. Noting the critical role of public confidence in the financial sector, this paper assessed the confidence levels in FS of Ghana, using Asante Mampong Municipality as a case study. A random sample of 384 respondents was used. Due to the ordinal nature of the dependent variable (confidence levels), the Partial Proportional Odds (PPO) model was used when the ordered logit model failed to pass the proportional odds assumption. About 46.4% of the respondents reported having ‘no confidence’ in the financial institutions of the country, while 37% indicated having ‘somehow confident’ in the sector. Less than 20% of the respondents expressed ‘confident’ (13.3%) or ‘very confident’ (3.4%) in the FS. Duration of engagement with a financial institution, loss of investment, awareness of crisis/reforms of the financial sector and income levels affected the confidence levels in the financial sector. Financial institutions are recommended to strengthen their relationship with customers by providing improved services and policy measures that secure customers investment/savings to ensure sustained and increased levels of confidence.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".