The odyssey of women in university education: stochastic growth models for studying access and graduation process
Bibliographic record
Abstract
This research aims to explore the evolution of statistics related to the proportion of women in university student populations (focusing on bachelor’s or equivalent degree programs) over an extensive period, spanning from the founding of the first university until recent times. This is done by utilizing empirical insights derived from comprehensive data series, presented here for the first time and primarily encompassing Greece, UK, Canada and USA. Motivated by the distinctive properties of these series and their sigmoid pattern, we delve into a general family of stochastic growth models for data analysis. A notable contribution of this paper lies in offering a novel interpretation of this family within a competing cause scenario, similar to those used in the theory of event history analysis. We emphasize on key common elements within the two statistical domains of research, thereby highlighting potential future research directions; besides, the interpretability of model parameters stands out as an advantage compared to other approaches such as those based on regression model theory. For assessing the proposed parameter estimation methodology under realistic conditions a simulation study is also conducted. The application and results are aimed at making a significant contribution to research in higher and university education by presenting comprehensive sets of consistent data and statistical tools to capture their fundamental characteristics and trends. Additionally, they can serve as valuable resources for education policy-makers and human resources planners alike.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".