Human resource management as a profession in South Africa
Bibliographic record
Abstract
Orientation: Various countries recognise human resource (HR) management as a bona fide profession. Research purpose: The objective of this study was to establish whether one could regard HR management, as practised in South Africa, as a profession.Motivation for the study: Many countries are reviewing the professionalisation of HR management. Therefore, it is necessary to establish the professional standing of HR management in South Africa.Research design, approach and method: The researchers used a purposive sampling strategy involving 95 participants. The researchers achieved triangulation by analysing original documents of the regulating bodies of the medical, legal, engineering and accounting professions internationally and locally as well as the regulating bodies of HR management in the United Kingdom (UK), the United States of America (USA) and Canada. Seventy- eight HR professionals registered with the South African Board for People Practices (SABPP) completed a questionnaire. The researchers analysed the data using content analysis and Lawshe’s Content Validity Ratio (CVR).Main findings: The results confirm that HR management in South Africa adheres to the four main pillars of professionalism and is a bona fide profession.Practical/managerial implications: The article highlights the need to regulate and formalise HR management in South Africa.Contribution/value-add: This study identifies a number of aspects that determine professionalism and isolates the most important elements that one needs to consider when regulating the HR profession.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".