Spotlight on...Dr. Barbara J. King
Bibliographic record
Abstract
Dr. Barbara King’s studies include observations of monkeys in Kenya and great apes in captive settings in Africa and the US. Her most recent book. How Animals Grieve, focuses more narrowly on the behaviors of elephants, cats, and birds. Her close examination of various animals has furthered the premise that animals have deeper emotional relationships than previously considered. She claims that through the writing of books like How Animals Grieve she has “brought together my love of animals and my love of anthropology in writing books about what it means to be human.” She frequently appears on interview programs across the world, in countries as wide-spread as Canada, Austria, Germany, the United States, and Australia. She is the recipient of numerous teaching awards from both the college of William & Mary and the state of Virginia. She received her BA in Anthropology from Douglass College and both her MA and PhD in Anthropology from the University of Oklahoma. She is currently the Chancellor Professor of Anthropology at the College of William and Mary.In the annual Lynch Humanities Lecture, Dr. Barbara J. King will present her lecture, "How Animals Grieve," on Thursday, April 30, at 5:00 pm in the Lecture Hall of the Doudna Fine Arts Center.
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 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.177 | 0.141 |
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".