Surviving the Sexodus Project: How STEM Women Approach Career Challenges
Bibliographic record
Abstract
That many women leave science somewhere between undergraduate studies and senior scientist positions has been well noted. Honest discussion of the challenges women face within and outside the STEM pipeline is required to highlight the reasons women advance to senior STEM positions in lower numbers than men. Importantly, such discussion offers a unique opportunity for women who have “been there” to share how they have coped with career obstacles and aid others facing the similar problems. The “Surviving the Sexodus” project began as a discussion between long‐time friends of how they walked the paths of two very different scientific careers. It resulted in an overwhelming response from other women scientists willing to share their stories to support, mentor and encourage others. In this talk we will examine the patterns and themes that have emerged from these stories. Why do women want to share their stories in this way and what do they feel it can offer to colleagues and students? Which survival strategies emerge at different career stages and which cross disciplines to be widely valuable for women? We will address these trends and how they fit with the larger culture of support for women in science and share insights both from our own stories and those that have been told to us.
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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.016 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".