IDENTIFYING INTERNAL MOBILITY PATTERN DIFFERENCES 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
During their career, members of the Canadian Armed Forces (CAF) are frequently assigned to a new position and can even change profession. This high, but normal, level of mobility within the CAF can potentially hide indicators of mobility pattern differences among women and men. This article presents a method to study the mobility of the CAF members over the last decade. We propose a series of statistical tests to detect trends and identify a list of conditions for which the mobility indicators differ from one subpopulation to another. Our analysis uses Fisher’s exact test to compare various populations for which sizes can vary from very small (less than 50) to large (tens of thousands). Since our approach allows us to do tests of statistical significance on small samples, we were able to perform a detailed breakdown of the CAF population (down to the occupational level).
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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