From queue to café: Tangerine’s interventions in Canada’s digital banking
Bibliographic record
Abstract
Traditionally, banking institutions have relied on face-to-face encounters to provide trusted and credible financial services. However, in today’s digitally-orientated marketplace, this is no longer an exclusive option. In Canada, the banking industry has be resistant to adopt these newer digital platforms and this introduces a number of potential setbacks for the nation’s economic and international interests. At the same time, the unique and emergent Canadian banking institution, Tangerine, has responded positively and productively to the changes. The bank, I argue in this Major Research Paper, thus offers a valuable case study for an analysis of new models of agile banking. By depicting the narrative of Canada’s banking history, investigating industry market documents and reports, then visually analyzing logos and branding strategies using theories from branding and visual semiotics, the MRP provides a comparison of Tangerine’s branding and infrastructure relative to Canada’s ‘Big Five’ banks (BMO, RBC, CIBC, TD, Scotiabank). Compared to the Big Five, Tangerine’s strong leadership, customer loyalty, and integration of digital practices make its intervention in Canada’s banking industry truly disruptive and as such, a model for 21st century banks to come.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.040 | 0.013 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".