Propensity to Join the Royal Canadian Navy
Bibliographic record
Abstract
Recruiting is an essential component of successful human resources management and is used to influence individuals to apply to organizations (Barber, 1998). As such, it has been an important focus of the Canadian Armed Forces (CAF). In 2008, the Canadian Federal Government announced a new defence strategy, which included increasing the size of the CAF to 70,000 Regular Force (Reg F) and 30,000 Reserve Force (Res F) personnel. In order to meet the increased personnel requirements, the CAF implemented a new recruiting and advertising campaign, which included initiatives such as more outreach activities to schools and job fairs, the revamping of the CAF recruiting website, and the development of new commercials ; all of which helped showcase the present role and focus of the CAF. While the new initiatives were quite successful with regard to recruiting Canadian Army (CA) personnel, 1 the recruitment of Royal Canadian Navy (RCN) personnel, particularly in certain occupations, had remained an area of concern, leading to issues such as undermanning on ships. To counteract this, the RCN engaged in a number of specific marketing and attraction strategies designed to increase awareness of the RCN, as well as promote it as a preferred career choice. This included the Great Lakes Deployment tour (tours of Her Majesty’s Canadian Ships [HMC Ships] to the cities bearing their names), the development of commercials prominently featuring RCN operations, and a host of activities to highlight the Canadian Naval Centennial, whose theme was “Bring the Navy to Canadians” (Canadian Naval Centennial, nd). In conjunction with this, Director Naval Personnel (D Nav Pers) engaged the research unit of the Department of National Defence (DND) to conduct an extensive study into the reasons why individuals join the Royal Canadian Navy. Although that research covered a wide spectrum of attraction and recruiting factors, the current study is focussed on a subset of these. Specifically, this study aimed to capture the reasons why recruits applied to the RCN in particular, and whether the influence of others or recruits’ prior familiarity with the RCN impacted their decision to apply.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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 teacher head, 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".