Overcoming the Glass Ceiling Syndrome through Digitalization and Artificial Intelligence in OECD Countries
Bibliographic record
Abstract
Intelligence in OECD Countries ABSTRACT This study examines the impact of digitalization and artificial intelligence technologies on the invisible barriers affecting women's access to senior management positions within the framework of the glass ceiling syndrome in OECD countries. Sweden, Germany, South Korea, Canada, and Turkey were selected as case studies, each evaluated through in-depth analysis of the public sector, private sector, and the activity reports of relevant institutions. The study applies a comparative assessment based on gender equality indices, digitalization strategies, and artificial intelligence applications, focusing on the relationship between women’s representation in digital leadership and the perception of the glass ceiling. The findings reveal that digitalization alone does not ensure gender equality; on the contrary, in countries where artificial intelligence technologies lack gender sensitivity, existing inequalities are likely to persist or deepen. While Sweden and Canada appear to have largely overcome the glass ceiling syndrome, the examples from South Korea and Turkey suggest that cultural and structural barriers continue to reinforce it. Ultimately, this study underscores the necessity of integrating gender-sensitive artificial intelligence technologies into institutional transformation efforts to overcome the glass ceiling in the digital age. Keywords: Digitalization, Artificial Intelligence, Glass Ceiling Syndrome, Gender Equality, OECD Countries Jel Code: J16, O33, M14 Referencing Style: APA 7
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".