The agricultural policy of Mexico in the american context (1995-2020)
Bibliographic record
Abstract
Objective: To analyze the long-term relationship of two groups of agricultural policy instruments classifiedby the OECD-Producer Support Estimator (PSE) and General Services Support Estimator (GSSE)-OECDclassification on Agricultural Gross Domestic Product (AGDP) in Mexico, USA, Canada, Chile and Brazilduring the period 1995-2020, to generate information that contributes to the design of agricultural policies.Design/Methodology/Approach: The information used in this work was developed by the OECD and wasintegrated into a time series for the 1995-2020 period. A quantitative analysis was carried out based on theeconometric method, applying the cointegration test.Results: The Canadian, Brazilian, and Mexican series are cointegrated, because the error of the model has aunit root (i.e., individual variables are not of order I(0)); however, the combination of their variables show thatthe error is a process I(0), with a zero mean. However, the Chilean and USA variables were not cointegrated.Study Limitations/Implications: An open market environment requires the development and implementationof policies that include the use of diverse and relevant instrument groups, guaranteeing that the resourcestransferred to the sector generate the expected results.Findings/Conclusions: In comparison with the PSE, the GSSE has a closer long-term relation with thegrowth of the agricultural GPB in most countries; therefore, using this group of instruments to transferresources to the sector is assumed to improve its performance to a greater degree.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".