The Paradox of Attracting Key Talent in the Canadian Cannabis Industry: Turning Over a New Leaf
Bibliographic record
Abstract
Abstract In October 2018, the Canadian federal government legalized the use of recreational cannabis with a goal to drastically diminish the black-market and the use of cannabis by minors. The attraction of talent to the new industry has been recognized as important to long-term industry success, but there exists a paradox in talent attraction. Key talent must first be screened by the Royal Canadian Mounted Police. Anyone with serious criminal charges in the past may not be cleared to work in the industry, blocking out experienced cannabis talent. Additionally, some potential talent may not be interested in working the legitimized industry although others may welcome the opportunity to work in it. HR managers have a rare opportunity to be trailblazers by establishing the norms for the industry. Their role should be established in the boardroom, but they will have to demonstrate their value through their ability to build talent in an industry made up largely of SMEs. We use a nested model of macro and micro TM perspectives to analyze the context of this industry. At the macro level we investigate how legalization, government regulation, legitimacy, and reputation affect TM within the micro level context. We suggest how HRM strategies related to attraction, development and retention can impact TM. The integration of the macro and micro level context of TM is paramount to the survival of the new legalized cannabis industry.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.000 |
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".