Gender Diversity and Inclusion Efforts within the Canadian Armed Forces
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
Historically, the armed forces and the military in general, have been perceived as male-dominated institutions. This is supported by various statistics, which indicate that women usually constitute up to 20% of the military personnel in a given country. As of May 2023, women comprised slightly more than 16% of members of the Canadian Regular Force. The number has been steadily increasing. What is Canada’s strategy for integrating gender equality into its armed forces? Is the objective solely to increase the numbers? Is it only about the women? The aim of this chapter is to present and examine gender inclusion efforts within the Canadian Armed Forces (CAF), as well as to assess how they align with the Canadian government’s broader feminist approach. The paper provides an overview of the current state of gender integration in CAF, including statistical data on the representation of women in various military roles and branches. It also examines CAF’s organizational culture and climate, including attitudes towards gender diversity, experiences of harassment and discrimination, and efforts to foster a more inclusive environment. Secondly, this chapter analyzes the policy framework and institutional mechanisms that are in place to promote gender equality within CAF, including the Gender-based Analysis Plus (GBA+) approach and the Women, Peace, and Security agenda. Furthermore, this paper discusses support services and resources available to military personnel, such as Gender Advisors, diversity and inclusion training, and policies addressing gender-based violence and harassment.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.019 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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