Diversity in the Danish Armed Forces
Bibliographic record
Abstract
The Danish Armed Forces face the functional imperative of becoming a smaller, professional expeditionary force and the societal imperative of including women and ethnic minorities. It currently lags behind its NATO partners in gender and ethnic diversity. Lessons to be learned from NATO members with more diverse militaries, such as the United States, Great Britain, and Canada, include recognition of diversity as a societal imperative to sustain the legitimacy of the armed forces, the necessity of systematically collecting and reporting personnel data to guide policy, the necessity of patience and realistic goals, systematically developing recruitment, development, and retention policies, and the superiority of an all-volunteer force over conscription in fulfilling this societal imperative.
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.
How this classification was reachedexpand
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 | 0.014 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.251 | 0.016 |
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; both teacher heads agree on what is shown here.
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".