False Face Must Hide What the False Heart Doth Know: Review and Model of Workplace Microaggression
Bibliographic record
Abstract
Microaggression contains elements of workplace aggression, bullying, incivility, stigmatization, and ostracism. We argue that studies of the phenomenon should be broadened to cover all workplace members. Anyone with distinctly different ascribed status, physical and/or psychological characteristics from the mainstream may be subjected to negative micro-acts. We address two questions of concern: Why microaggression has not been recognized to be as problematic in “normalized” work settings as with bullying and harassment, and how workers exposed to such micro-aggressive acts might respond. Theories of signal detection and coordinated management of meaning are used to explain how targets attribute the reasons for these negative acts, and manage to either mitigate or prevent them. We suggest ways of reducing the prevalence of microaggression by coordinating the management of meaning between the perceiver-as-target and the perpetrator. This leads to adaptive coping (Marrs, 2012) and fosters supportive workplace climates (Kim et al., 2018). Implications for workplace practices and policies are provided.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".