Net effects: examining strategies for women’s inclusion and influence in ASX200 company boards
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Conventional approaches to improving the representation of women on the boards of major companies typically focus on increasing the number of women appointed to these positions. We show that this strategy alone does not improve gender equity. Instead of relying on aggregate statistics (“headcounts”) to evaluate women’s inclusion, we use network analysis to identify and examine two types of influence in corporate board networks: local influence measured by degree centrality and global influence measured by betweenness centrality and k-core centrality. Comparing board membership data from Australia’s largest 200 listed companies in the ASX200 index in 2015 and 2018 respectively, we demonstrate that despite an increase in the number of women holding board seats during this time, their agency in terms of these network measures remains substantively unchanged. We argue that network analysis offers more nuanced approaches to measuring women’s inclusion in organizational networks and will facilitate more successful outcomes for gender diversity and equity.
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.
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 | 0.005 | 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 | 0.005 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it