Is a “Good Death” at the Time of Animal Slaughter an Essentially Contested Concept?
Bibliographic record
Abstract
The phrase "essentially contested concept" (ECC) entered the academic literature in 1956 in an attempt to better characterize certain contentious concepts of political theory. Commonly identified examples of contested concepts are morality, religion, democracy, science, nature, philosophy, and certain types of creative products such as the novel and art. The structure proposed to identify an ECC has proven useful in a wide variety of deliberative discourse in the social, political, and religious arenas where seemingly intractable but productive debates are found. Where a strongly held moral position is contradicted by law, a portion of the citizenry see the law as illegitimate and do not feel compelled to respect it. This paper will attempt to apply the analytic structure of ECC to the concept of animal wellbeing at the time of slaughter specifically a "good death." The results of this analysis supports an understanding that the current slaughter debate is a disagreement in moral belief and normative moral theory. The parties to the dispute have differing visions of the "good." The method of slaughter is not an essentially contested concept where further discourse is likely to result in a negotiated resolution. The position statements of veterinary organizations are used as an example of current discourse.
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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 | 0.007 | 0.012 |
| 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 | 0.036 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".