The Effects of R&D on Agricultural Productivity of Australian Broadacre Agriculture: A Semiparametric Smooth Coefficient Approach
Bibliographic record
Abstract
This article analyses the role of research and development (R&D) in Australia's broadacre farming by using the semi-parametric smooth coefficient model proposed by Hastie and Tibshirani (1993) and Li et al (2002) and a state-level dataset covering the period 1995 to 2007. While the conventional production function approach only captures the direct effects of R&D, this methodology captures both the direct impact of a change in R&D on output and the indirect impact through changes in efficiency of use of factor inputs in the production process. The empirical results show that once both the direct and indirect effects are taken into consideration, R&D investments significantly increase outputs. The results also show that there are substantial variations in the effects of R&D on output across the states. Such variations need to be taken into account when designing policies for investing public R&D in agriculture.
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How this classification was reachedexpand
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".