Bibliographic record
Abstract
Open Banking allows consumers to take advantage of data-driven financial services by sharing data held at one organization with another organization, typically between financial institutions and trusted third parties. Open Banking is consumer controlled, secure, and protects privacy. These new services represent an innovative and growing market. Clear and fair rules, industry coordination, and technical standards are needed to avoid fragmentation and to build a robust market that serves all consumers and Small and Medium Enterprises (SMEs). International developments in jurisdictions such as the UK and Australia have demonstrated the expected benefits that Open Banking can deliver to consumers. The challenge to governments in North America is to adopt a consistent framework that provides the security and protections consumers need while at the same time providing flexibility for innovation and streamlining of banking services using the Open Banking model. A key question for implementation of Open Banking will be the balance of activity and involvement between government and the private sector. An unbalanced Open Banking model will likely fail; a balanced Open Banking model can bring tremendous value to society. This teaching case asks students to first understand the benefits and challenges of Open Banking for many stakeholders, and then to recommend on how to proceed (or not) with implementation. The case is written from a North American perspective, that is, the USA and Canada.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.021 | 0.011 |
| Insufficient payload (model declined to judge) | 0.045 | 0.019 |
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".