Bibliographic record
Abstract
Abstract Although public sector special audit and performance audit are frequently involved in blame, very few studies (save for Radcliffe 1997) provide detailed empirical accounts on how auditing participates in blame allocation. This study sets out to study one case of blame allocation by describing and characterizing the origins of failure and antecedents leading to the need for blame allocation, the institutional entities and arrangements that participate in the blame game, and how these entities, including the supreme audit institution, are mobilized in the processes of blame allocation. Applying a case methodology with Actor–Network Theory principles, the study extends Hood's (2002, 2007) research on blame and blame avoidance strategies by showing how a blame‐frame evaluates and allocates blame. The contribution of the paper is in four parts: first, it reveals the mechanisms that cause scapegoating of particular people and the role of auditors as experts in such mechanisms; second, it assists to develop an understanding of some factors at the core of the “accountability paradox” noted by Roberts (2009); third, it contributes to explanations as to why failing public sector reforms survive controversy and scandal since a scapegoating process can “reboot” reforms by erasing the reform's problems; and fourth, it demonstrates that an understanding of blame can be a useful addition to Actor–Network Theory.
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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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".