The Case for Veteran- Friendly Higher Education in Canada and the United Kingdom
Bibliographic record
Abstract
This paper presents the case for greater effort to encourage former armed forces members, otherwise known as veterans, to access and thrive in higher education institutions in Canada and the United Kingdom (UK). By looking at existing research, almost exclusively conducted in the United States (US) and Australia, it proposes that similar efforts should be applied in Canadian and UK contexts. Whereas the US has developed educational opportunities and policies for this community since the inception of the 1944 GI Bill, Australia and Canada seem only now to be increasing attention in this area, while the UK appears not to be doing so at all. Building on this lengthy, primarily US research base and attention, along with nascent investigation and recommendations in Australia, the authors consider how both Canada and the UK might develop similar initiatives. These include targeted marketing and financial packages aimed at veterans, improved monitoring and support for them, and the creation of student veteran and staff associations and other peer support mechanisms. It is argued that this will not just benefit the student veterans concerned, but also the institutions they choose to study with, and the wider Canadian and UK societies they inhabit.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.029 | 0.013 |
| Scholarly communication | 0.017 | 0.005 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".