Narrative Limits of Moral Accounting: An Exploratory Analysis of the Financial Crisis Inquiry
Bibliographic record
Abstract
In 2009, the United States created the Financial Crisis Inquiry Commission (FCIC) to investigate the causes of the so-called global financial crisis of 2007-2008. This paper examines the FCIC’s interview with Lloyd Blankfein, Chairman and CEO of Goldman Sachs. Initially, we interpreted certain recurring patterns in the language employed by Blankfein as tactics used to “evade” moral responsibility. However, re-examining the inquiry proceedings using a critical discourse perspective led to surprising insights. Examining the discourse around moral responsibility as involving account-seeking (by the FCIC) and account-giving (by Blankfein) suggested that narrative limitations may be inherent to the accounting process, making it nearly impossible to fully account for the (im)morality of one’s actions. Any discourse on moral responsibility that fails to acknowledge such limits is liable to perpetrate violence of one kind even as it seeks to redress another.
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 | 0.023 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.013 | 0.022 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".