Out of the Box? How Managing a Subordinate’s Multiple Identities Affects the Quality of a Manager-Subordinate Relationship
Bibliographic record
Abstract
Positive manager-subordinate relationships are invaluable to organizations because they enable positive employee attitudes, citizenship behaviors, task performance, and more effective organizations. Yet extant theory provides a limited perspective on the factors that create these types of relationships. We highlight the important role subordinates also play in affecting the resource pool and propose that a subordinate’s multiple identities can provide him or her with access to knowledge and social capital resources that can be utilized for work-based tasks and activities. A manager and a subordinate may prefer similar or different strategies for managing the subordinate’s multiple identities, however, which can affect resource utilization and the quality of the manager-subordinate relationship. Our variance model summarizes our predictions about the effect of managers’ and subordinates’ strategy choices on the quality of manager-subordinate relationships. In doing so we integrate three divergent relational theories (leader-member exchange theory, relational-cultural theory, and a positive organizational scholarship perspective on positive relationships at work) and offer new insights on the quality of manager-subordinate relationships.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".