BUILDING CIVIL SOCIETY WITH FORMER MILITARY AND THEIR FAMILIES: THE ROLE OF HIGHER EDUCATION IN BRIDGING THE GAP BETWEEN MILITARY SERVICE AND CIVIL LIFE
Bibliographic record
Abstract
Introduction. While the military is viewed differently in Canada and Ukraine, inclusion of veterans into civil society is important for both countries. Transition from the military service to civilian life can be challenging. Therefore, the role of different institutions and organizations, that focus on trying to improve what is available for former soldiers and their families has to be discussed. The purpose of the article is to explore the role that higher educational institutions can and should play in assisting former military to better integrate into civilian society – civil society. Methods. Researchers conducted a literature review of journal articles and other relevant written materials as well as informal interviews with key informants. Results. Using the mixed methods of literature search, informal interviews with key informants, and observation, the article considers the way “veterans” are conceptualized in both Canada and Ukraine and how two particular universities in Canada and Ukraine now attempt to meet the needs of former military members, wondering how their needs may differ and be similar to other students of higher education. Originality. The article concludes that, since civil society in general has a responsibility to support veterans in their transition, and notes that there are gaps in both understanding of need and awareness/availability of appropriate resources, a full needs assessment is the next step. Conclusion. The authors recommend a pilot needs assessment at the LPNU in Lviv Oblast where a number of veterans have made their homes.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".