The Embodiment of Insult: A Theory of Biobehavioral Response to Workplace Incivility
Bibliographic record
Abstract
This article builds a broad theory to explain how people respond, both biologically and behaviorally, when targeted with incivility in organizations. Central to our theorizing is a multifaceted framework that yields four quadrants of target response: reciprocation, retreat, relationship repair, and recruitment of support. We advance the novel argument that these behaviors not only stem from biological change within the body but also stimulate such change. Behavioral responses that revolve around affiliation and produce positive social connections are most likely to bring biological benefits. However, social and cultural features of an organization can stand in the way of affiliation, especially for employees holding marginalized identities. When incivility persists over time and employees lack access to the resources needed to recover, we theorize, downstream consequences can include harms to their physical health. Like other aspects of organizational life, this biobehavioral theory of incivility response is anything but simple. But it may help explain how seemingly "small" insults can sometimes have large effects, ultimately undermining workforce well-being. It may also suggest novel sites for incivility intervention, focusing on the relational and inclusive side of work. The overarching goal of this article is to motivate new science on workplace incivility, new knowledge, and ultimately, new solutions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".