<i>Both</i> Diversity <i>and</i> Meritocracy: Managing the Diversity‐Meritocracy Paradox with Organizational Ambidexterity
Bibliographic record
Abstract
Abstract This conceptual paper addresses the diversity challenge organizations face as they seek to enhance opportunities for marginalized groups without damaging fairness perceptions for advantaged groups. This challenge stems from societal‐level conflicts between advantaged and marginalized groups which generate a paradoxical tension between the values of diversity and meritocracy. The diversity‐meritocracy paradox manifests in interaction as an identity validation‐threat system so that events benefitting marginalized groups threaten advantaged groups and vice versa. However, diversity and meritocracy are also interrelated, and fulfilling each of these values supports the other through their beneficial effects on organizational justice. Managing the paradox entails supporting perceptions of process integrity with diversity practices while supporting perceptions of individual competence with meritocracy practices. Balanced combinations of practices create organizational ambidexterity to fulfil diversity and meritocracy pressures simultaneously. Research is needed examining how organizations leverage the interrelatedness of diversity and meritocracy to achieve diversity, inclusion, and justice among employees.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| 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".