Selling the Canadian Forces' brand to Canada's youth
Bibliographic record
Abstract
Purpose This purpose of this paper is to facilitate the exploration of marketing strategy in general and branding strategy in particular for a non‐profit, governmental institution. Design/methodology/approach Students are taken to 2005 when the Canadian Forces needed to increase recruitment. Canada's ageing population and the war in Afghanistan were just two of the many reasons driving an immediate focus on signing up new young Canadians. However, the task was proving more difficult than anticipated. Findings A particular challenge lay in that the army's brand – always conservatively constructed to reflect the more peaceful side of military life – had served to alienate many would‐be soldiers who interpreted this portrayal as patronizing and boring. However, a new campaign focused on the more militaristic realities of war might have served only to put off the families of potential recruits to whom these youths turned for advice and support. With the face of the military presented largely through its recruitment campaigns, the Canadian Forces' marketing department needed to do some introspection in order to determine how to proceed. Originality/value This case serves to highlight the importance of branding and marketing strategy in a non‐traditional setting and related prompt discussion and learning. This case is intended for classroom use only. It is not intended to demonstrate effective or ineffective handling of a business situation.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".