Bibliographic record
Abstract
Counterproductive work behavior (CWB) is an umbrella term that refers to a wide range of acts conducted by employees that are harmful to organizations and their stakeholders. Whereas some specific acts of CWB, most notably withdrawal behaviors such as absence and turnover, have been investigated for decades, the emergence of the study of CWB as a broad class of behaviors is a recent development. Unfortunately, the literature on behaviors that can be classified as CWB is broad and disjointed and is in need of better integration. As we will note, there have been several terms used to refer to conceptually distinct but operationally overlapping if not identical constructs that are often studied in isolation from one another. Our goal in this handbook chapter is to provide an integrative overview of the literature that links the various forms of CWB that have been studied in the literature. We will begin with an overview from a historical perspective of the different concepts that can be subsumed under the CWB term. Second, we will discuss measurement issues and how CWB has been studied. Third, we will discuss potential antecedents of CWB that arise from the work environment and the person. Fourth, we will discuss potential consequences of CWB to organizations and stakeholders, including individual employees, groups, and customers/clients. Finally, we will take a look forward and suggest areas that need attention by CWB researchers.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".