Bibliographic record
Abstract
"The Tanner Lectures are a collection of educational and scientific discussions relating to human values. Conducted by leaders in their fields, the lectures are presented at renowned institutions around the world, including the Universities of Oxford, Harvard, and Yale. In January 2019, University of Toronto's Michael Lambek, professor, former Canada Research Chair, and member of the Royal Society of Canada, delivered the Tanner Lecture at the University of Michigan's Department of Philosophy on the topic of "Concepts and Persons." As well as tracing his career in social and cultural anthropology, Lambek's Tanner Lecture spoke on the intersection of anthropology and philosophy as a means of articulating the moral basis of human action. By elucidating where anthropology and philosophy might intersect, Lamberk's lecture is a profound examination of the human condition, and is beautifully captured in this publication. Concepts and Persons recounts the lecture as delivered at the prestigious event, the commentary of three distinguished respondents, and Lambek's own response to that commentary. The book's presentation of the lecture also includes a rich and layered set of notes that augment the lecture significantly, as well as additional clarification and thought that has developed since the event."--
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
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".