Furthering the Metaphor of the Leadership Labyrinth: Different Paths for Different People
Bibliographic record
Abstract
Although the workforce has become more diverse, there is still a predominance of White men in positions of senior leadership. This is incongruent with social norms and values, evolving population demographics, and political movements (e.g., the MeToo and Black Lives Matter). Further, it suggests that, despite progress in shattering the glass ceiling, there may still be obstacles to senior leader roles for people who vary from the expected demographic profile. Eagly and Carli (2007a) addressed this issue for women by developing the labyrinth metaphor; however, few researchers have explored how the labyrinth and its inherent challenges might apply to others. We strive to broaden the discussion of the labyrinth metaphor, increasing its applicability, by considering two types of labyrinths—unicursal and multicursal. We also deepen the theoretical foundation by positioning the labyrinth metaphor within two prominent organizational perspectives: institutional theory and social identity theory. Relying on arguments culled from these theories, we build a typology consisting of four different categories of challenges—identity, acceptance, access, and expectations—leaders may face on their path to senior leader roles. We then explicate how these challenges create differential paths for individuals who align with the traditional leader prototype and those that do not. We believe that awareness of the underlying mechanisms of the challenges leaders may face will particularly help create solutions to these obstacles faced by individuals who are diverse in gender, race, sexuality, religion, and other important factors.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.049 |
| Scholarly communication | 0.007 | 0.018 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".