Consistent and low is the only way to go: A polynomial regression approach to the effect of abusive supervision inconsistency.
Bibliographic record
Abstract
The literature on abusive supervision largely presumes that employees respond to abuse in a relatively straightforward way: When abuse is present, outcomes are unfavorable, and when abuse is absent, outcomes are favorable (or, at least less unfavorable). Yet despite the recognition that abusive supervision can vary over time, little consideration has been given to how past experiences of abuse may impact the ways employees react to it (or, its absence) in the present. This is a notable oversight, as it is widely acknowledged that past experiences create a context against which experiences in the present are compared. By applying a temporal lens to the experience of abusive supervision, we identify abusive supervision inconsistency as a phenomenon that may have different outcomes than would otherwise be predicted by the current consensus in this literature. We draw from theories on time and stress appraisal to develop a model that explains when, why, and for which employees, inconsistent abusive supervision may have negative outcomes (specifically, identifying anxiety as a proximal outcome of abusive supervision inconsistency that has downstream effects on turnover intentions). Moreover, the aforementioned theoretical perspectives dovetail in identifying employee workplace status as a moderator that may buffer employees from the stressful consequences of inconsistent abusive supervision. We test our model using two experience sampling studies with polynomial regression and response surface analyses. Our research makes important theoretical and practical contributions to the abusive supervision literature, as well as the literature on time. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".