Let My Hair Be Me: An Investigation of Employee Authenticity and Organizational Appearance Policies Through the Lens of Black Women’s Hair
Bibliographic record
Abstract
Appearance policies — formal or informal dress codes that set organizational expectations for how employees “should” appear at work (Pratt & Rafaeli, 1997; Society for Human Resource Management, 2016) are typically based on Eurocentric ideals of professionalism (Bell & Nkomo, 2003). Appearance policies are often enforced by well-intentioned managers striving to foster a professional workforce (Society for Human Resource Management, 2016), yet such policies may conflict with increasing organizational efforts to encourage employee authenticity. The current paper investigates how men, the primary decision-makers in the workplace, evaluate Black women’s Afrocentric hair at work. The paper focuses on Black women because they are often at the bottom of the workplace hierarchy (Catalyst, 2016b) and are confronted with both gender and racial inequities. The paper focuses on hair because it is a visual display of identity (Opie & Phillips, 2015) and fashion (Barnard, 2014) that may reflect how individuals choose to express their authenticity (Opie & Freeman, 2017). Further, hair is subjectively evaluated based on societal notions of professionalism, making Black women’s hair a helpful, intersectional lens through which to investigate the gendered and racialized bounds of workplace appearance.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".