WAGE DIFFERENTIALS IN THE CZECH AGRICULTURAL SECTOR IN THE PERIOD OF THE 1ST QUARTER 2000 TO THE 3RD QUARTER 2012 AND LABOR PRODUCTIVITY
Bibliographic record
Abstract
Long lasting wage disparity in agriculture has a negative effect on the number of workers in this sector and thus rural demographic development. The causes of the external income disparities in the agricultural sector according to economic theories have changed over time and currently each determinant of wage disparities in agriculture reaches a significance of various sizes. This paper carries out the calculation of the gross external wage disparities in the agricultural sector compared to the values for the total economy. The main objective of this paper is to estimate the significance of the influence of labor productivity in agriculture, calculated as the ratio of gross value added and total employment in the agricultural sector, on wages in agriculture using linear regression model of the 1st differences of the variables, estimated by ordinary least squares method. One solution could be to increase the labor productivity.
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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".