Editorial Overview: Exploring Leadership, Mentorship, and Gender in Academia: Insights from Recent Research
Bibliographic record
Abstract
The 2024 Volume of Advancing Women in Leadership Journal (AWLJ) includes researchers from around the world. They are from Canada (Ontario and British Columbia), the Philippines, Australia, and the United States, including California, Maryland, New York, North Carolina, and Ohio, and the work that is published here also highlights women from these locations and Trinidad and Ghana. These researchers dealt with structural impediments to women’s opportunities, highlighting the transformative power of mentorship, resilience, and targeted strategies against professional obstacles. This is a valuable collection because it underlines how gendered expectations, cultural biases, and organizational structures continue to be essential factors in the construction of women's leadership experiences. Yet it also points out some novel approaches, such as informal and peer mentoring, narrative inquiry, and digital advocacy, reflecting the potent role of collective action and relational support in promoting better levels of empowerment 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.
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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.015 | 0.016 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".