MEASURING THE AGRIBUSINESS DECISION ENVIRONMENT: CONSTRUCTING AN AGRIBUSINESS CONFIDENCE INDEX FOR SOUTH AFRICA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract South African agribusinesses are experiencing far reaching changes. Economists have developed indicators, generally known as indexes, to measure relative change in the industry. In this article the methodology to construct a business confidence index for agribusiness in South Africa is discussed. The objective of such an index is to determine the business confidence of agribusinesses as accurately as possible. Some 80 agribusinesses in South Africa are at present participating in the AGRIBUSINESS CONFIDENCE INDEX. For the first quarter of 2002 the index points to a 20% improvement in the business confidence of South African agribusinesses compared to the same period the year before, while in the second quarter the index was up 14% on the previous year. This increase in confidence goes hand in hand with some other positive trends in the agricultural sector of South Africa regarding competitiveness and investment.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it