Is Consistent and Low the Only Way to Go? A Commentary on the Study Made by Yoon et al. (2023)
Bibliographic record
Abstract
Yoon et al. (2023) took a polynomial regression approach to confront and challenge the extant knowledge on abusive supervision. Their theorization of the effect of abusive supervision inconsistency and their examination of two sampling studies lead to several conclusions. First, when it comes to abusive supervision, consistency matters: even a drop in the present may create anxiety. Second, when it comes to examining temporal phenomena, proper time lags may not matter: different time lags (i.e., 1 month in Study 1 vs. 1 week in Study 2) may generate converging effects. Third, managers should realize that to deal with abusive supervision, consistent and low is the only path to go. These conclusions, however, are based on the theorization of one aspect of congruence effects (i.e., focusing on a comparison between consistency and inconsistency) and the examination of one type of testing option (i.e., focusing on the principal axes to test lateral shift). In this commentary, we bring in a more complete theorization of congruence effects and different testing options. Our aim is to apply the polynomial regression approach in a more holistic manner to integrate Yoon et al.’s idea of abusive supervision consistency and the existing literature on abusive supervision, shedding new light on the key conclusions they made.
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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 | 0.033 | 0.170 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.019 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.011 | 0.005 |
| Research integrity | 0.067 | 0.085 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".