Human Resource Management a Function of the Past: A Content Analysis of the First Edition Canadian Introductory Human Resource Management Textbooks
Bibliographic record
Abstract
Current day Canadian Human Resource Management (HRM) textbooks had their beginnings during 1973-1984, when a number of United States (U.S.) authored and focused textbooks were adapted (or ‘Canadianised’) for the Canadian higher educational market, and one or two original Canadian texts were developed. The fundamental structure of the first Canadian edition introductory HRM textbooks continue to be adapted within educational and business environments three decades later, illustrating their significant influence on the education and discipline of HRM in Canada. This paper examines the intentions and approaches taken by the first Canadian textbook authors and their attempts to address what they saw as a relative lack of research to deal with issues facing Canadian personnel and the lack of material representative of the Canadian context, values, culture and working people. The paper reports on the results of a content analysis approach and compares each author’s intentions with the actual written content to reveal that despite author intentions the textbooks contained limited Canadian research, theories, context and culture. Also noted was the contents were focused on a narrowly functional perspective rooted in American theoretical developments that served to exacerbate the problem of Canadian content and perspective. This functional framework laid the foundation for current Canadian HRM textbooks.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.015 | 0.022 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".