El papel de las mujeres como actoras en las fuerzas armadas de América del Norte
Bibliographic record
Abstract
Introducción / Silvia Núñez García y Patricia Escamilla-Hamm; Introduction; Las responsabilidades y experiencias de las mujeres como actoras de las Fuerzas Armadas en América del Norte; Rear-admiral Jennifer Bennett, Royal Canadian Navy; Major General Gwendolyn Bingham, US Army; General Brigadier Irene Espinosa Reyes, Ejército Mexicano; Contraalmirante Irma de los Santos Ayala, Armada de México; Programas institucionales para promover la equidad de género en las fuerzas armadas de México, Estados Unidos y Canadá; Commander Amy R. Alcorn, US Navy; Teniente Coronel Rosa Elena Torres Dávila, Ejército Mexicano; Major Nancy Perron, Royal Canadian Air Force; Teniente de Navío Sandra Luz Navarrete Ramos, Armada de México; Mujeres en las fuerzas armadas de América del Norte: perspectivas, logros y desafíos; Capitán de Fragata Patricia Camacho Reyes, Armada de México; Lieutenant Colonel Sarah Russ, US Air Force; Mayor Judith Irina González Herrera, Ejército Mexicano; Major Krista Dunlop, Royal Regiment of Artillery, Canadian Army; Women in the canadian armed forces/Las mujeres en las fuerzas armadas canadienses; Ambassador Sara Hradecky (6/oct./2011-31/mar./2015); Embajadora Sara Hradecky (6/oct./2011-31/mar./2015); Semblanzas; Silvia Núñez García; Patricia Escamilla-Hamm
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".