Must Heads Roll? A Critique of and Alternative Approaches to Swift Blame
Bibliographic record
Abstract
When mistakes or perceived wrongdoings occur in the workplace, managers—like most human beings—demonstrate the tendency to locate someone to blame, including assigning responsibility and sanctioning perceived wrongdoers for their actions. We highlight that although this response can be motivated by organizational, legal, and psychological factors, blame can be detrimental to the organization and its employees when it occurs in a spontaneous and nondeliberative manner, which we label swift blame. We argue that swift blame can involve distorted perceptions and judgment, exacerbate conflict, erode employee engagement, and stifle organizational learning. We further argue that managers have a special responsibility to thoughtfully and carefully consider how they react to perceived wrongdoings. Drawing from dual processing theory of cognition, we propose that managers can respond more effectively by adopting perspectives that slow down these tendencies and promote more thoughtful reactions. To this end we highlight research opportunities for three alternatives to swift blame: (a) a no-blame approach, (b) systems of inquiry and accountability, and (c) mindfulness training.
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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 | 0.026 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.083 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.014 | 0.023 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".