Optimal Distinctiveness at Work
Bibliographic record
Abstract
Optimal distinctiveness theory (Brewer, 1991) proposes that individuals possess two fundamental and opposing needs – the need for differentiation and the need for assimilation – and often strive to achieve a balance between these two needs in various social contexts. While optimal distinctiveness theory has garnered much attention in the social psychology literature and its implications for social cognition, group membership identification and intergroup relations have been well examined (Leonardelli et al., 2010), its implications for individuals at work remain underexplored. This symposium aims to advance our understanding of how individuals navigate these two competing needs in the workplace. In particular, we aim to provide a novel perspective on why, when, and how people from diverse cultural contexts may pursue differentiation and assimilation, not only to fulfill their fundamental needs for individuality and belonging but also to respond to external material incentives. This symposium provides theoretical and practical insights for individuals striving to thrive psychologically and socially at work, as well as for organizations seeking to cultivate a workplace culture that is both productive and inclusive. How Performance Incentives Shape Workplace Authenticity Author: Alice Lee-Yoon; University of Missouri-Saint Louis Author: Julia D. Hur; New York University Author: Ashley Whillans; Harvard Business School Fit In or Stand Out? Race-Based Impression Management Strategy Effectiveness in Salary Negotiations Author: Kathy Vo; Northwestern University Author: Gabrielle Lopiano; Vanderbilt University Author: Tosen Nwadei; University of Toronto When Similarity Leads to Less Interpersonal Attraction at Work Author: Gaoyuan Zhu; Cornell University Author: John Angus Hildreth; Cornell University Author: Ya-Ru Chen; Cornell University Optimal Distinctiveness Theory’s Application in Chinese Management Research Author: Yanlong Zhang; Peking University Author: Jiani Wang; Peking University Author: Lanbing SHE; Peking University
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".