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 Waerp 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, Waerp 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, Waerp 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, Waerp 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).
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".