Bibliographic record
Abstract
Over the past few decades, the interdisciplinary field of human-animal studies, also known as animal studies, has become increasingly popular, evidenced by the growing number of journals and anthologies dedicated to this topic.With Dyr og mennesker i norsk litteratur [Animals and People in Norwegian Literature], Henning Howlid Wærp provides a valuable contribution to the field, using a variety of literary lenses and frameworks to examine the relationships between humans and animals in selected Norwegian fictional and non-fictional prose narratives, from the end of the nineteenth century up until today.While most chapters focus on novels, Wærp also devotes individual chapters to short stories, essays, and an exploration narrative.Wild animals, including mammals, insects, and birds, receive considerable attention, as do pets, and farm animals also appear.In his prologue, Wærp points out how he had to set boundaries, both in terms of genre and the types of human-animal relationships highlighted.In chapter one, which serves as an introduction, Wærp provides what he calls "generelle teoretiske refleksjoner" [general theoretical reflections] (9) on topics such as animals rights, the presence of animals in creative works, the nuanced and complicated relationships between human and non-human animals, and conservation.In doing so, he touches on creative works by Nordic authors ranging from Camilla Collett, Henrik Ibsen, and Sigrid Undset to Tove Jansson and Kerstin Ekman.He also references a variety of historians, literary critics, ecocritics, and philosophers, including Martha Nussbaum and her recently published Justice for Animals: Our Collective Responsibility (2023).Wærp continues to draw upon the work of these and other thinkers and critics in the thirteen chapters that follow, including Margaret Atwood, Erin James, Donna Haraway, and Arne Næss, framing his literary analyses in broad, yet nuanced, contexts.As Wærp points out in the prologue, the book's fourteen chapters can be read independently of each other and in any order.Reprints of animal-themed paintings, drawings, and other art works by mostly Nordic artists head each chapter, all containing an introduction where Wærp lays out his approach, a conclusion where we are left with questions to consider, and a bibliography.While Wærp does not use one theoretical framework or approach in this wideranging study (9), he does return to several themes throughout.These include the contemporary relevance of older literary works, the value of revisiting texts
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.132 | 0.043 |
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".