Form‐Based Practices and Counter‐Conduct in the Banking Industry<sup>*</sup>
Bibliographic record
Abstract
ABSTRACT This paper adopts a governmentality framing to examine how form‐based practices and counter‐conduct measures that permeate the banking industry are employed by bank executives who wish to be governed differently in a way that results in the control of customer conduct. Using the data set of the HSBC Case History Hearing released by the US Senate Permanent Subcommittee on Investigations on July 17, 2012, the paper shows how rationalities, programs, and technologies of governance are reproblematized, resulting in changed program policies and technologies. The study also makes two contributions to the literature. First, the paper contributes to our understanding of form‐based practices in the banking industry as a governance mechanism—specifically, how particular practices such as risk management, staff training, and customer training can be coupled with counter‐conduct to circumvent the rules. And second, by looking at rationalities, programs, and technologies, the paper suggests how executives problematize issues in a manner that leads to program changes and to changes in the governmental technologies that depart from the overarching rationalities of the industry.
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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".