Balancing Conflicting Roles in the Search for Legitimacy: The Professionalization of HRM in Canada
Bibliographic record
Abstract
Broad debates exist surrounding the professional status of the human resource management (HR) occupation. We address this by conducting a detailed exploratory case study of the current state of HR professionalization in Canada, guided by the theoretical frameworks of the trait and control models. Our findings demonstrate that HR practitioners are attempting to reconcile their potentially conflicting professional role with balancing employee and organizational interests through the development of professional associations, ethical codes of conduct, a body of knowledge and set of core competencies, and increasing requirements for training and certification. Further, data showing employer demand for the national-level HR certification suggest that these professionalization strategies have been somewhat successful thus far. We also present evidence suggesting that the profession has achieved some confirmation of legitimacy from post-secondary institutions and government. However, we offer cautionary recommendations to avoid stagnation or reversion as the professionalization process continues to evolve, particularly in light of comparisons with the philosophy and approach adopted in the United States. We highlight numerous implications for future research and practice surrounding the HR profession.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.028 | 0.011 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".