The Future of Androgyny: Could Extending Androgyny to Boards of Directors Help Manage Complexity?
Bibliographic record
Abstract
We seek to contribute and build upon Sandra Bem’s (1974, 1975) theory of androgyny by extending the concept from the individual to the team level of analysis. In this conceptual work, we highlight the past and present of androgyny and its measure by showing its effectiveness and success at the individual level using examples from the leadership and decision-making literature. Next, we examine the future of androgyny in illustrating the potential utility and importance of extending androgyny to the team level through examples focused on corporate boards of directors and complexity in organizations. Finally, we present levels of analysis arguments demonstrating why the concept could be extended and operationalized at the group level, and make suggestions regarding its potential measurement. Extending Sandra Bem’s androgyny theory and measurement (BSRI) to the team level has the potential to benefit both academics and practitioners seeking to study gender at the small group level in a variety of fields.
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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 | 0.019 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.007 | 0.044 |
| Scholarly communication | 0.010 | 0.024 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.006 |
| 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".