Military-connected students in higher education: A Canadian approach
Bibliographic record
Abstract
Canadian military-connected students are adult learners who maintain a significant tie to the Canadian Armed Forces (CAF) and who enter higher education without the benefit of purposefully crafted academic and social supports. When CAF service members move from the collective nature of the military to individual pursuits in civilian society, transition difficulties occur. While there is a dearth of Canadian-specific research on military-connected students, the US context can help contextualize this higher education issue for a greater understanding of inclusion difficulties. Based on a transformative research paradigm, this organizational improvement plan (OIP) looks to provide a voice to this underrepresented group of leaners in order to lead to an organization-wide recognition of the heterogeneity of military-connected students. Enabled by a transformational leadership approach at the macro-level of University X and an adaptive approach at the meso- and micro-levels, the OIP presents an interconnected implementation plan. The problem of practice (PoP) that drives the investigation is aimed at recognizing the diverse needs of military-connected students and cultivating a sustainable positive learning environment. The OIP will employ successive quality improvement cycles of a plan-do-study-act strategy to address the PoP. The desired outcome of the OIP is to link military-connected students to a supporting learning environment, peer support, and the local community through a harmonized institutional approach across all levels of University X.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.021 | 0.007 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".