The ‘Underdog’ as ‘Ideal Victim’? The Attribution of Victimhood in the 2007 Pet Food Recall
Bibliographic record
Abstract
Despite the increasing attention being paid to harm perpetrated against the environment and nonhuman animals within criminology, largely under the banner of ‘green criminology’, there has been little engagement with the victimology literature in researching and theorizing these forms of victimization. This paper uses the case of the 2007 pet food recall to demonstrate that an examination of harm perpetrated against nonhuman animals will require engaging with both the green criminological and victimological literatures. The findings of this study indicate that despite the popular cultural conceptualization of nonhuman animals as the ultimate ‘underdogs’ or ‘ideal victims’, their extreme level of vulnerability does not translate into the ascription of victimhood in newsprint media. This paper demonstrates that the bridging of green criminology and victimology provides a conceptual space to take seriously and theorize the ways in which animals are victimized and to illustrate how conceptualizations of victim and victimhood are socially, historically, and certainly species specific.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".