The Honour House Project: Reservist Re-Entry Program
Bibliographic record
Abstract
Both the United States and Canada invest a great deal of resources in the training of their military personal. Many of the skills and experiences accumulated by soldiers are those that are highly valued by civilian employers. Further, these skills are often embodied in academic programs, suggesting soldiers would have a comparative advantage in such programs; however, despite the efforts of government agencies, many soldiers are unable to convert their skills and training into meaningful careers. While there are several reasons why individuals leaving military duty have trouble re-integrating into work and education, one of the major obstacles is the difference between the military and civilian models of training and education. The differences create challenges to offering advanced placement or transfer credits for military training in civilian post-secondary institutions. This paper presents the findings from a pilot program at the British Columbia Institute of Technology (BCIT), Vancouver, Canada. The program uses an alternative approach to assessing military training for advanced placement into technology programs. Instead of the traditional course-by-course credit assessment, the program uses an integrated model that gives block credit or credential equivalence. This block credit is then used for advanced placement. Depending on the structure and field of the program being sought, the reservist receives significantly higher placement than would occur under most traditional models. The model is under review in the United States for application to GI Bill applicants transitioning from military service.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".