Financial institutions and the taxi-cab industry: an exploratory study in Canada
Bibliographic record
Abstract
A current challenge taxi-cab owner/operators face in Canada is the lack of financing for taxi-cabs. This article examines business opportunities and lending risk; it also provides risk management strategies for financial institutions to manage the risk of lending to the taxi-cab industry. Members of the boards of directors and shareholders from the Canadian taxi-cab industry, and lenders from financial institutions that do not provide financing to taxi-cab owner/operators, were interviewed. Board members and shareholders were asked about their perceptions regarding business opportunity, risk, and their willingness to provide collateral for taxi-cab loans. Lenders of financial institutions were asked about their reasons for not providing taxi-cab loans. The findings of this study show that there is a reasonably attractive opportunity for financial institutions to offer financing for taxi-cab owner/operators. However, the findings also show that there are both systematic and unsystematic risks in lending to the taxi-cab industry. This offers recommendations on risk management strategies for Canadian lenders to mitigate the risk in lending to the Canadian taxi-cab industry. Our findings may be useful for new and existing financial/lending institutions, lenders, investors, and taxi-cab owner/operators.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".