Mexico’s agricultural policy in the American context (1995-2020)
Bibliographic record
Abstract
Objective: To analyze the long-term relationship of two groups of agricultural policy instruments classified by the OECD-Producer Support Estimator (PSE) and General Services Support Estimator (GSSE)-OECD classification on Agricultural Gross Domestic Product (AGDP) in Mexico, USA, Canada, Chile and Brazil during 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 was integrated into a time series for the 1995-2020 period. A quantitative analysis was carried out based on the econometric method, applying the cointegration test.Results: The Canadian, Brazilian, and Mexican series are cointegrated, because the error of the model has a unit root (i.e., individual variables are not of order I(0)); however, the combination of their variables show that the 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 implementation of policies that include the use of diverse and relevant instrument groups, guaranteeing that the resources transferred to the sector generate the expected results.Findings/Conclusions: In comparison with the PSE, the GSSE has a closer long-term relation with the growth of the agricultural GPB in most countries; therefore, using this group of instruments to transfer resources to the sector is assumed to improve its performance to a greater degree.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 |
| 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".