Explaining the relational mechanisms and outcomes of multi‐modal leader–member‐exchange differentiation
Bibliographic record
Abstract
Abstract Research suggests that multi‐modal leader–member‐exchange (LMX) differentiation could be the most problematic pattern of differentiation. Therefore, we outline a conceptual model to explain how multi‐modal LMX differentiation can manifest as an LMX faultline—a special type of group faultline representing leader‐sourced social divides between a leader's preferred subgroup and nonpreferred subgroup(s) within a specified collective. LMX faultlines have dimensions of perceived multi‐modal LMX differentiation as well as faultline potency components of compositional diversity, unfairness of differentiation, and faultline agreement. We use LMX faultlines to explain how group members coalesce into subgroups based on concurrent forces of intra‐subgroup cohesion and inter‐subgroup polarization. Cohesion and polarization explain group‐level outcomes (coordination, performance, and viability), subgroup‐level insulation, and individual‐level outcomes (performance, well‐being, and conformity to the subgroup).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".