Called to Speak Up: How BIPOC Women Academics’ Improved Sense of Power Leads to Constructive Voice
Bibliographic record
Abstract
Constructive voice behavior aims at challenging an organization’s status quo and has been found to yield a myriad of individual- and organizational-level benefits. Despite these positive implications, speaking up is risky and can result in backlash for challenging existing power structures, particularly in university settings which are the focus of this investigation. These challenges are particularly pronounced for Black, Indigenous, People of Color (BIPOC) women academics who have historically experienced a reduced sense of power in higher education settings. Yet, little remains known about BIPOC women academic’s sense of power, constructive voice behaviors, and contextual factors. Drawing on approach/inhibition theory of power and theory of planned behavior, we advance a novel framework exploring the moderating role of calling orientation between BIPOC women academic’s sense of power and constructive voice behavior. We consider how the three psychological antecedents of voice behavior, namely attitude toward the behavior, subjective norms, and perceived behavior control, can be strengthened through calling orientation, motivating BIPOC women academics to enact constructive voice behavior. This relationship is examined through the two foci of calling, self-realization, and unification, which we posit will differently strengthen these psychological intentions of voice behavior. This paper present significant theoretical and practical implications.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".