Managerial Discretion and Constraints: A Bounded Leadership Model
Bibliographic record
Abstract
Purpose: We propose and test a new leadership model.Our model is an extension of the leaderplex model which proposes that leader cognitive and social complexities are linked with leader effectiveness indirectly, in a mediation scheme, through behavioral complexity.We enhance the leader plex model with a leader's degree of managerial discretion as the moderator of the links in this mediation format.Methodology: We test our model with a moderated mediation approach (BaronKenny fourstep procedure and PreacherHayes bootstrapping methods).Findings: We use results of interviews with top leaders in Poland and demonstrate that a leader's managerial discretion is a moderator affecting the mediation scheme assumed in the leaderplex model.Limitations: The sample size is only 29 leaders.To preserve the respondents' anonymity, their opinions were evaluated by only one researcher who interviewed them directly.The results may be country specific (Poland).Originality: We define new boundary conditions for the leaderplex model by showing importance of a leader's real position (managerial discretion) in an organization.Specifically, we show that the nature of the relationships between the variables of interest will change when a leader operates in one physical environment (e.g., high managerial discretion) rather than another (e.g., low managerial discretion).
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".