South Africa's wood processing industry education strategy: north south partnership to develop a globally competitive workforce for the 21st century
Bibliographic record
Abstract
The reintegration of South Africa into the global economy and technological change on many fronts has increased the need for investment in human capital to make South Africa's industry globally competitive. This paper describes how a partnership was formed between government, industry and educators in South Africa and Canada to develop university-level wood products education programmes capable of producing the managers needed to make South Africa's wood processing industry globally competitive. We describe the revitalisation of the professoriate at the Stellenbosch University and Nelson Mandela Metropolitan University, the institutions that offer these education programmes, and then describe how these institutions worked with the University of British Columbia in Canada to restructure their programmes. We also describe how the delivery of the restructured programmes is making use of new digital educational technologies to overcome the educational divide between academia and industry. The partnership with industry has been critical to the success of the wood processing industry educational strategy and we describe the strategies that were used to foster this partnership. We conclude by describing the outcomes of the partnership and the challenges that lie ahead for the strategy to realise its aim of helping to develop the human capital that South Africa's wood processing industry needs to be globally competitive in the twenty-first century.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".