Playing the Favorite Game: A Contextual Examination of Workplace Favoritism
Bibliographic record
Abstract
Workplace favoritism, in which a supervisor engages in ongoing preferential treatment of one or a few employees, is a common occurrence in many workgroups. Despite the prevalence of the phenomenon, however, workplace favoritism has not received much devoted scholarly attention in management research. Often the topic is studied either as one of many types of workplace mistreatment behaviors, through the lens of formal discrimination associated with nepotism/cronyism, or as a proxy when employees perceive dissimilarity in the quality of their relationships with their supervisor. Engaging in in-depth interviews with 77 individuals employed in the service industry and applying abductive methods, we uncover a previously unappreciated rich and complex set of interpersonal dynamics surrounding workplace favoritism. In this working model, we make sense of these dynamics by applying a role theory lens: conceptualizing workplace favorites as a special type of informal social role that can emerge in workgroups. How this role is enacted has important implications for non-favorites’ ongoing relationships with their supervisor, the favorites, and one another. Our research identifies five distinct favorite profiles that can emerge in workgroups and can range in terms of having a more benign versus antagonistic presence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".