Bibliographic record
Abstract
The executive branch of government constitutes the pinnacle of political power. In principle, presidents and prime ministers, along with their cabinets, set the policy agenda, debate, and deliberate policy initiatives; introduce legislation; and oversee the implementation of public policies. Executives are the most visible political actors, representing the public “face” of government. Until very recently, executives were also the most masculinized of political institutions, with women absent entirely from the position of prime minister or president until the 1960s, and, at least until the last decade, holding only a small number of posts in cabinet. Yet one of the most striking global trends in recent years is the growing number of women elected to the post of prime minister or president: at the time of writing there are 12 countries where a woman occupies the top political office. A growing number of women are also being appointed cabinet ministers and, in some cases, to some of the most traditionally masculine posts. It is common today to define “parity” cabinets as those where women hold between 40% and 60% of ministerial portfolios. With that definition, countries as different as Spain, Bolivia, Sweden, and South Africa have had gender parity in cabinet. What is more, women's presence in cabinet is now a firmly established norm. Among the first questions raised by commentators after a newly elected president or prime minister announces her cabinet are, how many women were appointed? To which portfolios were they assigned?
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.006 |
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".