Revealed Comparative Advantage and Competitiveness in Hungarian Agri-Food Sectors
Why this work is in the frame
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Bibliographic record
Abstract
We examine the competitiveness of Hungarian agriculture and food processing, in relation to that of the EU, based on four indices of revealed comparative advantage, using highly disaggregate data for the period 1992 to 1998. Consistency tests suggest that the indices are less satisfactory as cardinal measures, but are useful in identifying the demarcation between comparative advantage and comparative disadvantage. Hungary is shown to have a comparative advantage in a range of agri-food products, including animals and meat. This complements the findings of those studies that have used price and cost based approaches in identifying competitiveness in cereals and crops. Results indicate that the RCA indices, when interpreted as a binary measure, have remained surprisingly stable during the period of transition, although there is evidence of a weakening in the level of comparative advantage as revealed in the Balassa index.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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