Forbearance Leadership: “Doing Without Doing”
Bibliographic record
Abstract
A central premise of leadership research is that leaders accomplish positive outcomes through their actions – they inspire, guide, motivate, and coach. By contrast, inactive approaches to leadership are seen as ineffective and harmful. We introduce the concept of forbearance leadership to demonstrate that a leader’s purposefully inactive leadership behaviors can enhance leader effectiveness and follower job performance. Leaders exhibit forbearance leadership when they choose not to intervene, even though they are capable of acting and aware of the opportunity to do so. Building on theories of human development, we introduce two dimensions of forbearance leadership – forbearance learning and forbearance nurturing. We develop and validate a measure of the two dimensions of forbearance leadership and demonstrate how they are distinct from other passive leadership behaviors. Across four separate samples involving a total of 632 followers and 136 leader-follower dyads, we find support for our two-dimensional framework of forbearance leadership. Both forbearance behaviors are positively associated with affective trust and satisfaction with supervision and negatively related to role ambiguity. In addition, the congruence between leader-intended and follower-perceived forbearance leadership at Time 1 increased follower task performance and job dedication at Time 2.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".