Bibliographic record
Abstract
Open banking is successfully operating, and has proved beneficial, in many countries.But Canada has not yet adopted it. Alberta doesn’t need to wait for the federal government to implement a national framework to benefit from this innovation. Using international precedent, this article charts a pragmatic course for how the province can immediately participate in the benefits of open banking, open finance, and consumer data portability, without requiring a complex sub-national regulatory and governance structure like the federal approach or expending scarce provincial policy resources. Using a market-facilitative approach, the province can utilize existing initiatives to takean accommodative and active advisory role to data portability use case development, foster market-driven use cases and industry partnerships through the Financial Innovation Act (FIA) regulatory sandbox, and utilize the existing Invest Alberta Financial Services Concierge to reduce frictions and barriers to market entry for data portability firmsand open finance entrepreneurs. Open banking creates a safer underlying ecosystem to share consumer financial data, develop data applications and new technology-driven financial products and services ina more secure way than screen scraping. This innovation promotes competition, enhances consumer product comparisons, lowers switching and transaction costs, creates new efficiencies, and allows financial product and service providers to tailor new customer offerings to individual needs. The federal open banking framework still has many implementation barriers, uncertainties, and frictions. Alberta can immediately develop expertise in consumer data portabilityby using the provincial regulatory sandbox established by the FIA. The FIA is a one ofa kind initiative in Canada. The FIA sandbox allows banks and fintech companies to develop and test data portability use cases under supervised parameters with regulatory relief. Provincial regulatory authorities can review the risks and benefits in real time, with real data. The province can potentially leapfrog the national framework by developing expertise through the FIA sandbox in data portability use cases beyond banking and relating to a financial product or service (the defined legislative scope of the FIA). Technological development and applications, fostered through the FIA, may have use value beyond banking and within a larger financial ecosystem, as well as in energy, utilities, consumer retail data, government housed data and self-sovereign digital identity solutions. The province can take three immediate steps under a market-facilitative approach.First, engage in public-facing educational efforts on the benefits, use-cases, processes, accessibility, functionality, and successes of the FIA sandbox as applied to data portability. This may include developing principles for safe data sharing, and recommended design standards and guidance. Second, in conjunction with the FIA, utilize and promote the Invest Alberta Financial Services Concierge service as a gateway to open banking partnerships and the FIA sandbox. Third, investigate how to create and implement a provincial consumer data right (CDR), which would serve as a catalyst in the province for a myriad of data-portability use cases beyond banking to an open-data paradigm, including applications in energy, investments, insurance, utilities, telecommunications, consumer retail and self-sovereign digital identify.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".