Bibliographic record
Abstract
This book had its origins during work for an earlier volume in the Heritage Matters series, Changing Perceptions of Nature.From discussions about ideas of wilderness, and how our approach to landscapes, geology, plants and animals have changed over time, our thoughts began to focus on those animals that people had engaged with, been scared by, utilised, or adored, for centuries.Of all these iconic animals -including wolves, whales and elephants -one animal in particular stood out: the bear.We knew that there was a rich seam of information to mine here -not only on the biology of bears, but more importantly on the human-bear relationship and how this has been expressed in folklore, in first nations' societies, in childhood stories and toys.We wanted also to discuss how our perception of bears is influenced by the manner in which they are revealed to the public in the wild, in museums and galleries, and to give a message about just how threatened some species are, and why, and the efforts being made to conserve them.We also wanted to reflect on what our long association with bears says about us; to borrow from Shakespeare, we wanted to hold, as 'twere, the mirror up to [bears], to show virtue her own feature, scorn her own image, and the very age and body of the time his form and pressure.'The reflected image is contradictory, complex and at times frankly disturbing.To achieve these wide-ranging aims we have been fortunate to bring together a distinguished field of authors, all of whom have addressed bear species from different standpoints.To all of them we are extremely grateful for their time, expertise and patience, but especially so to the two doyens of bear studies: Barrie Gilbert and Lynn Rogers who have kindly provided the Foreword and Afterword for this book.Special thanks are due to Caroline Palmer at Boydell, who has been extremely patient with the Editors, keeping us on track,
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.489 | 0.343 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".