Global linkages, the higher education pipeline, and national contexts: The worldwide growth of women faculty, 1970–2012
Bibliographic record
Abstract
Despite the surge in women’s enrollments in higher education over the last several decades, women continue to be unequally represented in faculty careers around the world. In this article, we use data from the United Nations Educational, Scientific, and Cultural Organization (UNESCO) to examine and explain regional and global trends in percentages of women faculty within 92 countries from 1970 to 2012. Drawing on world society and development perspectives, we posit that women’s representation among faculty is influenced by a combination of global norms of justice and women’s rights as well as national contexts. Results of descriptive analyses show remarkable growth over time for all world regions, although gender parity has yet to be reached. Using country fixed effects panel regression strategies, we find that countries with higher levels of women who earn higher education degrees, stronger linkages to global norms of women’s rights, and higher levels of economic development are more likely to have higher percentages of women faculty, with the caveat that the effect of economic development is conditioned by national levels of women’s caregiving burdens. Although the pipeline argument serves as a popular narrative, women’s access to higher education is only part of the story; our analyses indicate that percentages of women faculty are shaped by the intersection of norms, national contexts, and human capital.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".