Contextualizing leaders' interpretations of proactive followership
Bibliographic record
Abstract
Summary Although proactive followership behavior is often viewed as instrumental to group success, leaders do not always respond favorably to the actions of overly eager followers. Guided by a constructivist perspective, we investigated how interpretations of followership differ across the settings in which acts of leadership and followership emerge. In thematically analyzing data from semi‐structured interviews with leaders of high‐performing teams, we depict how the construal of follower behaviors relates to various contextual factors underscoring leader–follower interactions. Prototypical characteristics were described in relation to ideal followership (i.e., active independent thought, ability to process self‐related information accurately, collective orientation, and relational transparency). However, proactive followership behaviors were subject to the situational and relational demands that were salient during leader–follower interactions. Notably, the presence of third‐party observers, the demands of the task, stage in the decision‐making process, suitability of the targeted issue, and relational dynamics influenced which follower behaviors were viewed as appropriate from the leader's perspective. These findings provide insight into when leaders are more likely to endorse proactive followership, suggesting that proactive followership requires an awareness of how to calibrate one's actions in accordance with prevailing circumstances. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".