Genderwashing: the myth of equality
Bibliographic record
Abstract
Genderwashing is an organizational tool that presents the myth of gender equality in organizations through discourse and text. To critique this organizational myth, this paper contributes a new perspective and theoretical definition for genderwashing. We outline how genderwashing activities are underpinned by economic over ethical motives, which are grounded in a desire to uphold an organization’s reputation, sometimes at the expense of employee well-being. Our presentation of genderwashing is grounded not only in the work of Critical Human Resource Development (CHRD), but also in Dorothy Smith’s concept of ruling relations. These lenses enable us to examine how superficial attempts to address gender inequality within organizations fail to create structural change or disrupt engrained power dynamics. Furthermore, drawing on Sara Ahmed’s work on diversity and institutional silencing, we argue that genderwashing represents an organizational stance that purports to practice equality, even while women and other marginalized individuals experience little or no advancement. In regards to gender inequalities, we focus on how one organizational text, the Non-Disclosure Agreement (NDA) works to reinforce gender inequalities in organizations. We conclude with a call to action for practitioners and scholars offering suggestions for how to move beyond the stultifying practices of organizational genderwashing.
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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 | 0.018 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.109 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".