Bibliographic record
Abstract
ABSTRACT We examine whether management faultlines (i.e., dissimilar groupings among executives) are related to management forecast processes and outcomes. Management faultlines are formed based on the simultaneous alignment of senior executives' demographic characteristics (e.g., an MBA background, elite school education, gender, board experience, age, or tenure). We argue that management faultlines impede information sharing, create conflicts, and divert managerial attention away from common‐goal tasks. We hypothesize and find that management faultlines are associated with lower management forecast quality. Furthermore, the faultline effect is more pronounced when forecasting difficulty is high. In contrast, the faultline effect is mitigated when a firm nurtures a supportive and diverse workplace. In addition, we find that management forecast propensity and frequency are negatively associated with management faultlines. Overall, our findings suggest that management faultlines compromise management forecast processes and outcomes. In particular, since faultlines can arise as a company diversifies, boards should be aware of these unintended consequences and how they can be mitigated.
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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 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".