An eye for an eye? A meta-analysis of negative reciprocity in organizations.
Bibliographic record
Abstract
Most models of negative workplace behaviors (NWB) are individual in nature, focusing on individual attitudes (e.g., satisfaction) and general workplace perceptions (e.g., procedural justice) that motivate NWB. Less commonly considered are explorations of relationally based negative workplace behaviors-how NWB from Party A is related to reciprocation of NWB from Party B. Based on 2 competing conceptualizations in the literature, that behavior is reciprocated "in-kind" in an eye for an eye exchange or that behavior tends to escalate or spiral over time, we develop a framework for negative reciprocity that considers NWB in terms of severity, activity, and target. This framework addresses (a) whether Party A's NWB is associated with behavior of a similar or greater level (i.e., activity and severity) from Party B; and (b) whether Party B's reciprocating behavior is directed back at Party A (i.e., direct) or transferred onto others (i.e., displaced). We meta-analytically test these relationships with 246 independent samples (N = 96,930) and find strongest support for relationships indicating that NWB from Party A is largely returned in-kind, followed closely by relationships indicative of escalation. We also found that as the frequency of Party A's NWB increases, so too does the frequency of reciprocity behavior of equal levels. Surprisingly, differences related to the target of the behavior as well as differences based on whether the data were cross-sectional or longitudinal were generally negligible. (PsycINFO Database Record (c) 2019 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".