Introduction: North American Women in Politics and International Relations
Bibliographic record
Abstract
Isabelle Vagnoux1 "We can't be more machista than the Argentines," former President Bill Clinton reportedly quipped in 2008, when his wife Hillary Rodham Clinton was battling in the Democratic primaries of the presidential election, shortly after Cristina Fernandez de Kirchner had been elected President of Argentina in 2007, and following Michelle Bachelet's election in Chile the year before.The United States more 'machista' than Latin America in politics ?A challenging issue that was tackled during the French Institute of the Americas' annual conference, December 4-6, 2013, which gathered over 150 scholars working on Women in the Americas at Aix-Marseille Université, France.The selection that follows was part of a panel devoted to Women, politics and international relations in the Americas.Only those focusing on the United States and Canada are presented in this issue of the European Journal of American Studies.While four pieces are devoted to a variety of aspects of women's representation and influence in politics, three others focus on American women in international relations or diplomacy.1. Of underrepresentation 2 Empirical research and statistics show indeed that the United States is not faring very well in terms of women's political representation and influence although significant nuances may exist between the local, state and national levels.As of August 2014, the Interparliamentary Union ranks the United States 86th out of 152 with a meagre 18.2% in
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.053 | 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".