El papel de las mujeres como actoras en las fuerzas armadas de América del Norte
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it