Men, Women, and Leadership Centralization in Groups Over Time.
Bibliographic record
Abstract
The authors propose a model for predicting the emergence of group norms from the demographic composition of groups. They use this model to study gender and leadership centralization in groups over time. Results from 2 longitudinal studies were consistent with their predictions: (a) Women, more than men, preferred equality norms in groups; (b) all-male and majority-male groups had relatively centralized leadership structures; (c) all-female groups had relatively decentralized leadership structures; and (d) balanced and majority-female groups were relatively centralized at the onset of group interaction but decreased in centralization over time. Much research and theory has addressed how being male or female influences an individual’s chance of emerging as a leader in a small group or team (cf. Carli & Eagly, 1999; Eagly & Karau, 1991). Little is known, however, about how the sex composition of a group influences its emergent leadership structure: that is, whether a group develops a more centralized structure, whereby leadership is concentrated in one or a few group members, or a more decentralized structure, whereby leadership is shared among members (e.g., Pearce & Sims, 2002). This is an important issue to understand, as centralized structures have been linked to lower levels of group cohesion (Widmeyer, Brawley, & Carron, 1990), satisfaction (Porter & Lawler, 1964), and performance (e.g., Bloom, 1999; Janis, 1989) and to higher levels of tardiness, absenteeism, and turnover (Durand, 1985; Spink & Carron, 1992). Moreover, conventional wisdom and some theories suggest that a group’s leadership structure is shaped by its sex composition. This article represents one of the first contributions to this endeavor. We propose a dominant norms model of how a group’s sex composition determines its leadership centralization, both early in its development and over time. This model is based on the idea that individuals with different social characteristics and backgrounds prefer different interaction norms in groups. It considers these preferences, along with differences in status between the social groups in question, to predict which norms will initially and eventually dominate group interaction. We test the model with two longitudinal studies of groups with different sex compositions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".