Political knowledge at work: Conceptualization, measurement, and applications to follower proactivity
Bibliographic record
Abstract
In this paper, we conceptualize and integrate a measure of political knowledge into the broader literatures on political behaviour, proactivity, and followership. Political knowledge refers to an individual's perceived understanding of the relationships, demands, resources, and preferences of an influential target, such as their leader. We examine political knowledge in the follower–leader context with two studies of employees ( N s = 301 & 492) and two studies of follower–leader pairs ( N s = 187 & 130 dyads). Findings generally support the convergent and discriminant validity of our political knowledge measure. In addition, we find consistent evidence for the mediating role of political knowledge of one's leader in the relationship between follower political skill and political will with self‐reported follower proactive behaviours. Taken together, the results contribute to the political influence framework and offer insight into the importance of ‘knowing your leader’ in enabling followers to engage in politically risky proactivity. Practitioner points Political knowledge describes an individual's understanding of specific influential others’ relationships, demands, resources, and preferences. Followers with political knowledge are more likely to take charge and enact change, which we think is because this knowledge makes enacting change seem less risky. Leaders seeking to improve their followers’ political knowledge should focus on building high‐quality relationships with followers; these relationships are positively associated with political knowledge.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".