Effect of income diversification on Canadian credit union performance.
Bibliographic record
Abstract
The main objective of this thesis is to measure the impact of income diversification on Canadian credit union performance. Although credit union functions as an integral part of the Canadian economy, very few studies have focused solely on the Canadian credit union. Core money lending activities are still the primary source of earning, but the scope of non-interest income is increasing. In this thesis, we use return on asset (ROA) and risk-adjusted return on asset as a measure of profitability and risk-adjusted profitability in our model. In the robustness test, we use return on equity (ROE) and risk-adjusted rate of return on equity as our profitability metrics. In this thesis, the coefficient of non-interest income reflects the impact of non-interest-bearing activities on credit unions’ performance. We see that the effect of revenue diversification for ROA and risk-adjusted ROA are positive. It suggests that non-interest-bearing activities have a strong positive relationship with our dependent variable. We see a similar kind of relationship with ROE and risk-adjusted ROE. We find that income diversification has a more substantial impact on larger credit unions than smaller ones.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".