Bibliographic record
Abstract
Abstract Even though eco-production is based on principles brought by IFOAM (International Federation of Organic Agriculture Movements), the standards which were brought in by the national and supranational governments may differentiate in parts of regulation. The mutual recognition/equivalence of eco-standards of the EU (»EU-eco« label, based on regulation of the European Commission EZ 834/2007: 139-and EZ 889/2008: 173-256, and other regulations derived from them) and Canada (»Canada organic« label, based on valid Canadian eco-standards, regulation CAN/CGSB-32.310-2015: 53 and CAN/CGSB-32.311-2015: 75) has been present for multiple years and has been re-evaluated and confirmed in 2015 as a successful practice in the increase of access to an expanded market for producers, increase of selection for consumers and lightening the regulatory cooperation. Before mutual recognition exported eco-product from Canada to the EU (and vice-versa) had to go through recertification, which created additional expenses for exporting eco-producers (10 thousand dollars per year, on average). This process mostly resulted in an increased price of eco-products for the end consumer. In some areas the Canadian eco-regulation is stricter than the EU one, while in other it is vice versa. Some markings can mislead the consumer, especially the one who does not read the product declaration where such misgivings are clearly visible and marked. The greatest challenge for eco-production in the EU is the increase in demand for eco-products with such a speed that EU farmers cannot satisfy it, which inevitably leads to an increase of import from non-EU countries. Therefore, the help of EU governments is essential in the form of support for farmers who decide to transition into eco-production. Certain estimates say that the CETA could mean a loss of a great number of producers (estimating that it could be several thousand workplaces in agriculture across the EU). A similar agreement between the US and Mexico already led to a loss of workplace for 2 million people in Mexico in the midst of inability to compete with the industrial production of the US. The greatest fear present in eco-production is that the international agricultural businesses can force national and supranational governments to lower standards by using lawsuits, which can consequentially result in lower standards in eco-production on both sides of the Atlantic and influence the environment. It is not based on the scientific/expert arguments which governs the ecological agriculture, but a pure race for profit. Therefore, it can be expected that, once again, »greed overcomes reason«. Nevertheless, the high set »bar« of eco-production »from both sides of the pond« is the best »defence« against the fear that CETA will bring any novelties into the life of eco-producers.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.078 | 0.015 |
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".