Bibliographic record
Abstract
Women hold only 7.8% of the supervisory board posts in the 200 biggest companies (Top 200) in Germany - outside finance -, and three of four (76.0%) are worker´s representation delegates. More than one third of these companies do not have a woman on the supervisory board at all. The share of women on management boards is even smaller. In the 100 biggest companies (Top 100) there is only one woman on a management board. There are only eleven in the Top 200, a share of a good one percent. These figures show that the aim of equality of opportunity for men and women in top influential posts in large firms is still a long way off. Among European countries Norway is in the lead with women accounting for just under one third of the seats on the decision-making bodies of the 50 biggest companies traded on the stock exchange. The other Scandinavian countries are also above the average, as are the East European EU member states. Germany is in the middle with 11%. According to information from the European Commission Germany is at the lower end of the scale of countries in the share of women in more broadly defined management posts, with around one quarter. Even countries with a relatively low percentage of women in employment, like Spain and Italy, have clearly higher shares of women on this level of the hierarchy than Germany, with just under one third each.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.051 | 0.008 |
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".