Towards Critical Human Resource Management Education (CHRME): a sociological imagination approach
Bibliographic record
Abstract
This article explores the professional standing of the discipline of human resource management (HRM) in business schools in the post-financial crisis period. Using the prism of the sociological imagination, it explains the learning to be gained from teaching HRM that is sensitive to context, power and inequality. The context of crisis provides ideal circumstances for critical reflexivity and for integrating wider societal issues into the HRM curriculum. It argues for Critical Human Resource Management Education or CHRME, which, if adopted, would be an antidote to prescriptive practitioner-oriented approaches. It proceeds to set out five principles for CHRME: using the ‘sociological imagination’ prism; emphasizing the social nature of the employment relationship; investigating paradox within HRM; designing learning outcomes that encourage students to appraise HRM outcomes critically; and reflexive critique. Crucially, CHRME offers a teaching strategy that does not neglect or marginalize the reality of structural power, inequality and employee work experiences.
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 | 0.014 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.049 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".