Evolution of the South African science, technology and innovation system 1994-2010 : an exploration
Bibliographic record
Abstract
This paper is part of a longitudinal project on the evolution of the South African science, technology and innovation (STI) system since 1994, the year that marked the end of apartheid government. It was hypothesised that the overarching national (and international) commitment of the post-1994 government to reform society to become an inclusive system serving the needs of all would impact on four of the main pillars of the STI system, viz. national policy objectives, funding of the system, the human resource composition (specifically race) and steering/control mechanisms. The research approach consisted of qualitative analyses of official published information. The analyses supported the hypothesis with regard to policy changes, the transformation of the human resource base, at least at managerial and executive levels, and the tightening of steering of the public STI system. It was found, however, that funding of the system relative to GDP in effect stalled while new policies were put in place. Funding has started rising moderately since 2001. The paper concludes by identifying six sets of developments that both individually and in combination would probably influence the further evolution of the STI system.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".