ANTH 151B Human Cultures Sage Spring 2024
Bibliographic record
Abstract
The discipline of anthropology in America is most often traced back to its father figure, “Papa Franz” Boas. A German psychologist and geographer by training, Boas was introduced to the Inuit peoples of Baffin Island and became fascinated by their culture. Eventually, he would work for the Field Museum, the American Museum of Natural History, the Smithsonian Institution, and head the first American Department of Anthropology at Columbia University in 1896. Through Boas, American anthropology would be defined as a field of four unique, but interrelated sub-fields – socio-cultural anthropology, linguistic anthropology, archaeology, and physical anthropology. During this course, you will explore and learn about the history, theories, methods, ethics and applications of socio-cultural anthropology. While this course specifically addresses the socio-cultural sub-field, you will also be exposed to linguistics, archaeology, and physical anthropology and how these other sub-fields inform socio-cultural anthropological research and understanding. “So…how does this relate to me?” Anthropology is the study of humanity in every aspect across time and space. If people ‘do it,’ ‘think it’ or ‘make it,’ then it is fair game for anthropological study. At one time, anthropology as a discipline was focused on and brought to mind images of the exotic ‘Other’ – societies and cultures in the most remote places on earth with practices and beliefs that both amazed and horrified those living in the modern Western world. And yes, today, research continues in some of the most remote areas of the planet, and with peoples who do have practices and beliefs strikingly different from mainstream Western culture. However, anthropologists are also engaged in research within the Western world, interested in the variety of sub-cultures that exist, and with the range of concerns and problems facing humanity from the local to the global. This course is designed to be an introduction to socio-cultural anthropology, so for those of you who will become anthropology majors this is your introduction to the concepts and methods that you will build upon as you progress in your career. Those of you that go into other fields will benefit from this course as well, because as you will see over the course of the semester the topics and questions of concern for anthropologists are everywhere! My goals for you as a non-anthropology major are to learn about how anthropologists approach and study the immense cultural diversity that exists in the world, to appreciate the uniqueness of specific cultures, to learn about the common threads that bind us all as humans, and to take this with you, applying the concepts, theories and methods to your respective field for a greater understanding.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.730 | 0.540 |
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".