Bibliographic record
Abstract
Abstract In recent years scholars of abusive supervision have expanded the scope of outcomes examined and have advanced new psychological and social processes to account for these and other outcomes. Besides the commonly used relational theories such as justice theory and social exchange theory, recent studies have more frequently drawn from theories about emotion to describe how abusive supervision influences the behavior, attitudes, and well-being of both the victims and the perpetrators. In addition, an increasing number of studies have examined the antecedents of abusive supervision. The studied antecedents include personality, behavioral, and situational characteristics of the supervisors and/or the subordinates. Studies have reported how characteristics of the supervisor and that of the focal victim interact to determining abuse frequency. Formerly postulated outcomes of abusive supervision (e.g., subordinate performance) have also been identified as antecedents of abusive supervision. This points to a need to model dynamic and mutually reciprocal processes between leader abusive behavior and follower responses with longitudinal data. Moreover, extending prior research that has exclusively focused on the victim’s perspective, scholars have started to take the supervisor’s perspective and the lens of third-parties, such as victims’ coworkers, to understand the broad impact of abusive supervision. Finally, a small number of studies have started to model abusive supervision as a multilevel phenomenon. These studies have examined a group aggregated measure of abusive supervision, examining its influence as an antecedent of individual level outcomes and as a moderator of relationships between individuals’ experiences of abusive supervision and personal outcomes. More research could be devoted to establishing the causal effects of abusive supervision and to developing organizational interventions to reduce abusive supervision.
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".