The Business Anticipatory Ecosystem outside the “First World”: Competitive Intelligence in South Africa
Bibliographic record
Abstract
The purpose of this article is to extend the Competitive Intelligence (CI) business ecosystem concept and measurements, as developed by our previous work, to South Africa. The article is based on a pioneer study on the CI business ecosystem conducted outside North America and demonstrates how the concept and measurements are applicable in other countries.The business ecosystem view considers the state of CI both in terms of intelligence practice (by firms) and the support system that enables firm practice. For this study, measures from past studies and additional revised measures were used to examine firms’ CI practice as well as CI supporting systems within government, academia, and professional associations. Through multiple lines of research, the study noted that CI remains a practiced discipline in South Africa with evidence of the field having evolved within the country. While CI practices have grown in terms of some elements (for example, academic contribution), activities in other aspects of the ecosystem have declined such as association involvement, conferences, workshops, and training. Future research should be conducted to better understand the changes in these elements and their impact upon CI practice.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".