BUILDING CIVIL SOCIETY WITH FORMER MILITARY AND THEIR FAMILIES: THE ROLE OF HIGHER EDUCATION IN BRIDGING THE GAP BETWEEN MILITARY SERVICE AND CIVIL LIFE
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it