Bibliographic record
Abstract
Service as a member of the Canadian Forces (CF) in 2010 is very demanding undertaking. The commitments the CF is fulfilling, both domestically and internationally, have placed a tempo on the CF that has not been witnessed in the decades since the Korean War. The recent publication of the Canada First Defence Strategy (CFDS) identifies personnel as the key resource to success in meeting defense commitments. The Government of Canada has pledged funding to the Department of National Defence (DND) to allow it to modernize, reorganize and expand the CF to meet its security responsibilities. The growth and expansion of the CF has been ongoing since early 2006. In the intervening period there have been huge successes realized in CF recruiting efforts; thousands of soldiers, sailors and airmen have been enrolled. However, despite a large increase in recruiting, the corresponding growth of the CF has been somewhat slow due to a high level of attrition. Attrition in the CF manifests itself in two broad groups; those recruited that never complete their basic occupational training, and those who are completely trained. Countless surveys and studies have identified macro reasons why individuals decide to cease their employment with the CF, many of which point to a breakdown in commitment to the organization as a result of not having individual needs adequately met. This monograph takes a different approach to looking at what motivates (or not) members of the CF to continue to serve. By focusing on two theories of motivation tied to the needs of today's modern solider, some specific recommendations are offered as to where CF Leadership should look to focus effort to strengthen and modernize the social contract with CF members so that they remain motivated to serve, thus meeting the needs of both the institution and the individual.
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.
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.021 | 0.004 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.032 | 0.005 |
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".