The case for change: Reimagining research to transform military culture
Bibliographic record
Abstract
Military culture change, driven by the imperative of meaningful inclusion of all military members, has been identified by the Department of National Defence and Canadian Armed Forces as a key priority. All who participate in defence-related research - especially the Canadian Institute for Military and Veteran Health Research - have a responsibility to think about what kind of research is needed to support military culture change. The research community is challenged to reflect on taken-for-granted practices and how they might need to be transformed to support change objectives. While reinforcing the critical role of Sex and Gender Equity in Research guidelines and Gender-based Analysis Plus in research, the discussion further emphasizes the required translation of diversity and participation into meaningful inclusion. This article foregrounds the pivotal role of research relationships and the co-creation of knowledge across participants, researchers, and stakeholder communities through accountable engagement at all stages of the research cycle, from conceptualization to the application of resulting knowledge. In summary, conducting meaningfully inclusive research in support of military culture change needs to focus on building community-wide competency and ensuring accountability to those with lived experience.
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 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 | 0.419 | 0.309 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.017 | 0.077 |
| Scholarly communication | 0.050 | 0.070 |
| Open science | 0.009 | 0.031 |
| Research integrity | 0.017 | 0.033 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier 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".