HRD as a social & discursive construct : exploring the significance of culture in HRD discourse
Bibliographic record
Abstract
Despite the rapid growth of interest in HRD as a potential source of organisational advantage, its development as a body of knowledge has been stymied by a lack of attention to the fundamental principles giving HRD its philosophical base. Much of the debate has centred on the dualism of the learning or performance orientation of HRD. While some research exists examining the role of culture in the areas of recruitment, organisational socialisation and training transfer, there is a critical lack of theoretical rigour and research related to the impact of cultural issues in the HRD field. Drawing on discourse analysis this working paper initiates a critical examination of the role of culture in HRD discourse from an interpretivist and social constructionist perspective.The growth of interest by organisations in HRD as a potential source of organisational advantage and its development as a body of knowledge has been undermined by a lack of attention to the fundamental principles from whence HRD draws its philosophical base (Barrie & Pace 1998; Anderson 1995; Boxall 1993; Blyton & Turnbull 1992; Butler 1991; Watkins 1991). Much of the debate has centred on the dualism of learning and performance orientation and the influence of other variables, in particular culture has been largely under-researched (Kuchinke, 1999, 1998). While research examines the role of culture in recruitment, organisational socialisation and training transfer (Huo & Von Glinow 1995; Lawrence 1994; Sparrow & Wu 1998), there is a lack of theoretical rigour and research relating to the influence of culture on HRD (Ashton et al. 2000; Hansen & Brook 1994; Kuchinke 1999; Maurice et al. 1986; McLean 1991; Peterson 1997; Saha, 1995). Drawing on discourse analysis this working paper tentatively initiates a critical examination of the role of culture in HRD discourse from an interpretivist and social constructionist perspective. Its purpose is to question the dominance of managerialist discourse and to surface some of its taken for granted assumptions.
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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 | 0.021 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.012 | 0.056 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| 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".