Evaluating assertions by a Wells Fargo CEO of a ‘return to ethical conduct’
Bibliographic record
Abstract
We explore the language used by the CEO of Wells Fargo, Timothy Sloan, to sustain claims that Wells Fargo and its staff would behave in an ethically appropriate way in the future. We focus on Sloan’s opening written statement to the Committee on Banking, Housing and Urban Affairs of the United States Senate on 3 October 2017. This statement was intended to salvage Wells Fargo’s reputation after it had been savaged by widespread allegations of unethical conduct. Sloan sought to display ethical leadership by drawing the senators’ and other stakeholders’ attention to an (alleged) epiphany in the managerial mindset of the company. This paper contributes by proposing three desirable hallmarks by which to analyze claims of ethical leadership: balanced framing, principled use of ideology and metaphor, and justified rhetoric. These hallmarks are then explored to assess the ethical leadership discourse in Sloan’s statement. The paper demonstrates that analysis of the micro discourse of a CEO can reveal lack of a sound appreciation of the human complexity of a large organization, superficial assumptions of leadership and followership, and cloud the location of agency for ethical lapses. We conclude that the written statement submitted to the senate committee by CEO Sloan was unconvincing in enlisting belief that Wells Fargo would ‘return to ethical conduct’.
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 | 0.024 | 0.119 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".