Bibliographic record
Abstract
In September 2011, Bank of America, squeezed by the debit interchange fee cap introduced by the Durbin Amendment of the Dodd-Frank Wall Street Reform and Consumer Protection Act, announced its intention to impose a $5 monthly fee to its debit card users (Rauch 2011 ). This brute force change in debit card pricing gave rise to a grassroots campaign (Lopez 2011 ), which quickly gained traction through social networks and caught the attention of mainstream media. Bank of America was not the only bank involved in this “debit fee fiasco”; Wells Fargo, JP Morgan Chase, Regions, SunTrust, Citibank and PNC all reportedly planned to introduce a $3–$5 monthly fee to their debit card users. As public backlash grew, a consumer protest movement called “Bank Transfer Day” (Pfeifer and Reckard 2011 ) sprang up and called for voluntary switches from retail banks to not-for-profit credit unions by 5 November 2011. The result was that on 1 November 2011, Bank of America announced that it would cancel its plan to charge its debit card users and the other banks backed off at around the same time (Sidel 2011 ). In January 2012, Bank of America CEO, Brian Moynihan, acknowledged in the fourth quarter earnings call that the “debit fee fiasco” resulted in a 20 percent jump in account closings at Bank of America (Kim 2012 ). Meanwhile, the National Association of Federal Credit Union reported a 700 percent increase in new account openings at affiliated credit unions in October 2011, compared with the same period the previous year (FOXBusiness, 2011 ).
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.126 | 0.039 |
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".