Coming together to combat food fraud: Regulatory networks in the EU
Bibliographic record
Abstract
Introduction As food fraud is international in scope, steps must be taken to ensure international cooperation in responding to food fraud. Food fraud is increasingly a policy priority for the European Union (EU) and its member states (European Parliament, 2016), leading to increased networking. This was stimulated largely by the horsemeat scandal of 2013 (FSAI, 2013), which provided a wake-up call to European policy-makers to ensure inter-EU and international regulatory networks were fit to respond to food fraud. This chapter examines how regulatory networks are used to prevent and respond to food fraud incidents, and argues that networked governance is essential in dealing with modern food crimes and harms. Networked governance is essential in the response to food crime. Food crime is not a new phenomenon (Paulus, 1974). It has the potential to damage both consumers’ safety and their economic interests. Consumers may be injured by food that has unsafe elements or that has been processed in an unhygienic environment (Spink and Moyer, 2011). For example, chemicals may be introduced into food products to provide desirable characteristics or meat that has been illegally slaughtered may be fraudulently placed on the market. Consumers’ economic interests will be damaged as they will be induced to pay for fraudulent food, which professes particular characteristics that it does not possess. For example, a consumer will pay more for Manuka honey than for other honeys, more for olive oil than other oils, or more for cod than other forms of white fish. When food is deceptively sold with valuable characteristics that it does not possess, enforcement bodies should intervene. However, regulators acting alone may not have either the information or the power to take action against the perpetrators of food crime, and may not have the geographical reach to remove deceptive food from our shelves and our homes. Article 8 of the EU's General Food Law (GFL) evinces an aim to prevent ‘fraudulent or deceptive practices.’ While there is no formal definition of such practices, they can be understood as ‘violations of food law motivated by the intention to obtain an undue benefit’ (European Commission, 2017). Spink and Moyer (2011, R157) define food fraud as ‘an intentional act for economic gain.’ Food fraud also amounts to a violation of Regulation 1169/2011 on food information, as information that accompanies fraudulent food will inevitably be misleading to consumers.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".