The impact of racial representativeness within human resources on employee perceptions that the hiring process is fair
Bibliographic record
Abstract
It is not enough for a hiring process to be equitable, for the organization to be healthy, employees must also believe that the process is fair. Merit-based assessment should provide greater assurances of fairness, but do not always translate into employee confidence in the process. The theory of representative bureaucracy holds that when individuals are represented by officials with decision-making power, they feel more confident that the process will be fair. This study examines whether racial/ethnic representation within HR increases employee confidence in the organization’s hiring process. To do so, it uses multi-level modeling to examine this relationship across employees within 27 organizations in the U.S. federal workforce. Contrary to theory and popular opinion, increased HR representation among Black/African-American, Asian, and White employees is associated with lower confidence that the organization either engaged in fair and open competition, selected the best qualified candidate, or recruited a diverse pool of applicants. This means confidence in the hiring process is not simply traced to current levels of under- or over-representation, employee perceptions of fairness are more nuanced. This prompts the question: How does an organization increase employee confidence in the hiring process if a more representative HR leads to negative results?
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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 | 0.003 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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".