Denunciation and resistance in post-crisis sensemaking
Bibliographic record
Abstract
Many stakeholders require some form of post-crisis sensemaking to help them better understand what happened, and why. Through this lens, we review public inquiry oral evidence sessions with the incriminated company leaders from three major business failures in the UK. While the stated objective of these public inquiries was to ‘learn lessons’, we mobilize Harold Garfinkel’s writings on ceremonies of degradation to offer an alternative perspective. We identify and discuss the strategies employed by the denouncers intended to lower the targets’ identities in the social order. We find that the denouncers frequently rely on accounting-oriented challenges. Following this, we explore the company leaders’ responses as they attempt to resist the denunciation. We identify four key resistance strategies: reframing, recalibration, refocusing, and blame-shifting/-sharing. As part of their response, the denounced provide their own accounting-oriented counter-explanations, emphasising that their choices were consistent with professional norms. We discuss the implications of these findings and what they mean for the management and maintenance of social order in the wake of a financial scandal. Specifically, we point to the inherent ambiguity built into established accounting norms which become a key battleground in the fight for the preservation (or ritual destruction) of the target’s identity.
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 | 0.047 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.012 | 0.065 |
| Scholarly communication | 0.028 | 0.029 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.007 | 0.008 |
| 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".