Precious blood and nourishing offal: past and present slaughtering perspectives in Sámi reindeer pastoralism
Bibliographic record
Abstract
Abstract In the Arctic, indigenous reindeer herding peoples rely on a pastoralist food and knowledge system that supplies them with protein, vitamins, and minerals. Reindeer pastoralism is a product of the interaction between animals’ physical needs, their behaviour, and the skills of the herders. The food systems of Sámi reindeer pastoralists depend on indigenous knowledge about mountain slaughtering. When the first stationary reindeer slaughterhouse opened in Guovdageaidnu (Northern Norway) in 1957, rationalisation of reindeer husbandry and methods of reindeer slaughter took place. Animal welfare and reindeer slaughter within slaughterhouses are well-documented in Norway; the historical knowledge about slaughtering reindeer in the mountains, however, is barely documented and is in danger of being lost. A qualitative study entailing interviews with five Sámi reindeer herders (50–80 years old) from Guovdageaidnu and Varanger explains indigenous, nomadic methods of killing and slaughtering reindeer. The traditional Sámi way of killing reindeer (Sámi: giehtadit ) was to pierce the heart with a large knife through the chest (Sámi: mielga ), particularly in the pastures close to where the herd grazed to avoid distressing the reindeer before taking their lives. This allowed reindeer herders to use the whole reindeer for food, unlike the practice in stationary slaughterhouses, which merely utilises select muscles for human consumption. Although the Supreme Court of Norway in 2008 ruled that traditional slaughter practice without stunning was illegal, this historical account provides evidence that the giehtadit method was a rational way to kill a reindeer, as bleeding (haemorrhage) in the thorax offers both high-quality blood, offal, and meat for human consumption. We conclude that the traditional Sámi method is based on systematic, complex, and holistic indigenous knowledge and determines the foods reindeer herders eat.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.008 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".