Crime versus harm in the transportation of animals: A closer look at Ontario’s ‘pig trial’
Bibliographic record
Abstract
Introduction On a hot June day in 2015, Anita Krajnc, an animaladvocate with Toronto Pig Save in Ontario, Canada,approached a truck transporting nearly 200 pigs.They were making the approximately 100 kilometre (60mile) trip to slaughter. She observed the apparentlythirsty pigs, and through the slats in the side ofthe stopped truck, gave the overheated animals somewater from a bottle. The truck driver reportedlyemerged and used his phone to video record what wastranspiring. The exchange between the two of them asrecorded went as follows: Truck driver: “Don't give them anything! Do notput anything in there!” Krajnc: “Jesus said ‘If they are thirsty, givethem water.’” Truck driver: “No, you know what? These are nothumans you dumb frickin’ broad! Hello!” (quoted inWang, 2016) This was not the first time that activists had givenwater to pigs being transported to thisslaughterhouse (Carter, 2016a). Nonetheless, thistime the driver called the police prior to takingthe pigs to the slaughterhouse. Krajnc was chargedwith criminal mischief, which carries with it thepotential of imprisonment (originally with apotential maximum of 10 years, but later reduced toa summary conviction offence with a maximum of sixmonths) and a fine of up to $5,000. The trial beganin August 2016; Krajnc pleaded not guilty. In thefirst week of May 2017 – eight months after thefirst court appearance (well above the average forsuch cases) – Ontario Court judge David Harrisdismissed the charge of criminal mischief. Although interesting in its own right, this caseprovides a useful context for exploring the legaland social constructions of ‘food crime’. If onewere to simply employ a legalistic definition, thiscase would be considered a food crime because of thepotential of food adulteration caused by Krajnc's‘criminal mischief ’. However, this chapter presentsthe argument that while this case is in fact a foodcrime, it is not because of Krajnc's actions. Itbegins by exploring two aspects of Canadian law thatprovide the backdrop for this case: the legal statusof animals as property, and transportationregulations that mandate the minimum amount of carerequired when transporting animals to slaughter.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.049 | 0.017 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.012 | 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".