Bibliographic record
Abstract
This chapter draws on evidence from our 3-year Economic and Social Research Council (ESRC) funded research study of the lives, careers, experiences, and aspirations of Generation X (under 40 years of age) principals and vice-principals in London, New York City, and Toronto. A review of literature pertaining to gender and leadership was undertaken. Data was gathered using networking events/focus groups and individual interviews. Across all three cities, leaders expressed consistent and worrying experience with finding a suitable and sustainable balance between their work and home lives. As most education systems are struggling to recruit and retain school leaders, the importance of ensuring a wide and diverse cadre of leaders gains importance. As women leaders are often historically underrepresented, ensuring aspiring women leaders have access to role models, support, and strategies to address their most pressing concerns about ascending to school leader roles are important. We will highlight how women leaders across our study experience, and are tackling work–life balance constraints; and share their advice on how to best achieve a work–life fit that works. We also identified, and will discuss, our emerging concept of a work–life balance hierarchy which has played out in the lives of participating leaders. This hierarchy exists implicitly and explicitly and often influences who is perceived to be deserving of work– life balance beyond school.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".