The Culture of Employee Learning- Which Way for South Africa?
Bibliographic record
Abstract
Employee learning which is known by terms such as human capital development and lifelong learning is an aspect of post-school learning which people engage in for purposes of enhancing their work-related competencies and possibly achieve upward professional mobility. There are different views on how best employee learning can be achieved at organizational and national levels, a debate which is currently alive in South Africa. The genesis of this debate is in the continued manifestation of aspects of the country’s history such as socio-economic inequalities as well as how employee learning can be used to narrow them. This article looks at the concept of the culture of employee learning in terms of the theories on the basis of which its character is determined. Two such theories are Human Capital Theory and Critical Realism. Through application of these theories to the South African context of employee learning, the article concludes by offering suggestions on the direction which the country could take to create a sustainable culture of employee learning which might assist with addressing, amongst other problems, the challenge of socio-economic inequality which continues to haunt the country twenty one years after 1994.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".