INTERNATIONAL EXPERIENCE IN SUPPORTING VETERANS’ EMPLOYMENT AND THE DEVELOPMENT OF VETERAN ENTREPRENEURSHIP
Bibliographic record
Abstract
The article presents comprehensive approaches to supporting veteran-owned businesses and facilitating the employment of demobilized military personnel in civilian life across various countries, with a particular focus on the experiences of the United States, Canada, and the United Kingdom. In the United States, a multi-level system of veteran support is implemented through a network of governmental institutions, where the Department of Veterans Affairs, the Department of Defense, and the Small Business Administration (SBA) play key roles. The main components of this system include: the Transition Assistance Program (TAP); peer-to-peer mentoring systems for veterans with PTSD; specialized employment services; support centers for service members and their families; educational programs such as “Boots to Business”; financial tools and preferential loans; special procurement quotas for veteran-owned businesses; and numerous information resources and portals. In Canada, veteran support is managed through the dedicated department Veterans Affairs Canada, with a focus on educational programs and mentorship. Of particular note is the “Operation Entrepreneur” program, funded by the government, financial institutions, and charitable organizations. The United Kingdom employs its own model, where public initiatives are complemented by the significant involvement of civil society organizations. Key instruments include: the Start Up Loans scheme; Heropreneurs mentoring support; the "Civvy Street" and "Be the Boss" programs; the work of the SSAFA Armed Forces charity; and the "Mapping the Needs" research project. The article demonstrates that the successful reintegration of veterans into civilian life requires a comprehensive approach, including financial assistance, educational programs, psychological support, mentoring, and the creation of special conditions for the development of veteran entrepreneurship, tailored to the specific needs of this population group.
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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 | 0.004 | 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 | 0.008 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".